Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

In this paper, we empirically evaluate the performance of state-of-the-art deep-learning methods such as RNNs, GNNs, and Transformers as well as the classic statistical ARIMA method.

The review comment to be evaluated is:

# Strengths
* The paper addresses an interesting spatiotemporal prediction problem.
* The paper attempts the important work of benchmarking a diverse collection of methods on a difficult task.
* The dataset which this paper will publicly release will no doubt be useful to the community, especially since it has data for before and during COVID.
* Investigating the use of side information (sparse geo-location information from tourists and metadata about weather and holidays) is worthwhile and interesting.

# Weaknesses
* The paper trains 7 different deep learning approaches with the same learning rate and reports the results. I believe it is uncontroversial to claim that tuning the learning rate is perhaps the most important step in training a deep learning algorithm. Given this, it is difficult to trust the results in the paper.
* It is not clear whether a validation set was used or (if a validation set was used) how it was constructed. Also, which split is used for model selection?
* The ARIMA model gets to use the true values for past time points before making a prediction. Do the deep learning models get a similar advantage? If so, how is it implemented?
* The results are not contextualized. At what point is an improvement meaningful? Are MAE and RMSE effectively capturing what is important for the end-user? For instance, in Table 1 we see that GRU-D is better than CT-RNN by 0.01 RMSE when doing "only visitors" prediction. Is this a meaningful difference for the use case? Does this represent a stable difference between the models, or would the results reverse if we trained again?
* There is no discussion of the potential for bias in the data. Is there a good reason to believe that Salzburg Card holders are a representative sample of the population of tourists? How is the mobile phone data sampled from the population? If these datasets are biased, what effects could they have on the models?

The evidence to be evaluated is:Existing work examines tourist demand forecasting using Recurrent Neural Networks such as LSTMs Rizal & Harati (2016); Li & Cao (2018) or hidden Markov Models in conjunction with deep neural networks Yao & Cao (2020). Most of these studies make predictions with only a limited set of models.

Existing work examines tourist demand forecasting using Recurrent Neural Networks such as LSTMsRizal & Hartati (2016); Li & Cao (2018) or hidden Markov Models in conjunction with deep neural networks Yao & Cao (2020). Most of these studies make predictions with a limited set of models.

We then perform predictions with a rich set of models and do a comprehensive comparison of the results.

Then we go over the methods we chose to evaluate and compare their performances. We use a large set of RNN variations on the tourist-flow dataset to perform a comprehensive comparison of the state-of-the art models and provide insight on their performance. The set comprises vanilla-RNN, LSTM, phased-LSTM, GRU-D, CT-RNN, CT-LSTM and Neural-ODE networks. Moreover, we used a Transformer model, using only the encoder part with 8 heads, 64 hidden units, and 3 layers, to forecast the tourist flow. Finally, we applied Attention Temporal Graph Convolutional Networks (A3T-GCN) Zhu et al. to our prediction problem in order to utilize geolocation data of individual tourists.

In this study we use the non-seasonal variant of the ARIMA model which does not consider the seasonal patterns in a time-series. This model is usually denoted as _ARIMA (\(p\),\(d\),\(q\))_, where \(p\) is the number of autoregressive terms, \(d\) is the number of non-seasonal differences, and \(q\) is the number of lagged forecast errors in the prediction equation Burger et al. (2001). A generic expression for the non-seasonal ARIMA process is given as Hyndman & Khandakar (2008): \[\phi(B)(1-B)^{d}y_{t}=c+\theta(B)\varepsilon_{t}\]

For this study, we have used the _auto.arima_ function from the _R forecast_ library that automatically fits the ARIMA model with different sets of parameters and returns the best combination of \(p\), \(d\), and \(q\) for the given time-series Hyndman & Khandakar (2008); Hyndman & Athanasopoulos (2018). Given that each of the 32 POIs in this study has a different time-series of visitor counts, we have determined different ARIMA parameters for each POI which were the best fit for its time-series. ARIMA predictions models were then build individually for each POI by fitting the ARIMA to that POI's training dataset with the best \(p\), \(d\), and \(q\) parameters for that POI using the Python _pmdarima_ library.

We used the Salzburg card data from years 2017, 2018, and 2019 for our first set of experiments.

Our results show partial success for the GNN approach since it was able to handle the very sparse tracking data which led to overfitting in all other models. We performed a diverse set of experiments with ARIMA and DL models to evaluate their forecasting accuracy, execution time and prediction time and compare the models. Table 1 shows the Mean-Absolute-Error (MAE) and Root-Mean-Squared-Error (RMSE) achieved for each method applied to the timeframe from 2017-2019 - before COVID. For the deep-learning models model size, corresponding number of parameters and training and prediction times of the best run are listed additionally.

All of our deep-learning models were able to outperform the ARIMA method in both metrics, with and without additional features.

ARIMA was not able to use the external features and fails when given only short sequences as input.

We measured training times for the DL models and prediction times for all models on a standard workstation machine setup (4 CPU cores with 3.9 GHz, 8GB DDR3 memory) equipped with a single GPU (GeForce GTX 1050 Ti, 4GB memory). As shown in Table 1 ARIMA took 69s to perform a single prediction for all POIs and the DL took for the same task fractions of milliseconds, while having used once y minutes for training. It is not possible to directly compare training and testing runtimes between them because ARIMA does not have a dedicated training step where the model is built. Instead, before every new prediction at time t, ARIMA model is fitted to the known time series up until the time t-1 (i.e., training and testing are integrated for every prediction).

ARIMA calculations are time-suming, since it makes predictions for each POI separately, while the DL models are trained with the visitors to all POIs in a single vector and make predictions for all at the same time.

In order to visually explore the predictions made by the models, we plotted the predictions and the ground truth for a few selected time-windows (see Figure 2).

Our plots show that although ARIMA is out-performed by the DL methods in the average error of all predictions, there are cases where it actually performs better than the other models.

As visible in the plot, the DL models show a better performance, especially in the valleys where the ARIMA fails to predict the downfalls accurately.

However, CT-RNN shows a very good performance in predicting the first and last peak and at least shows an upward trend for the second and third peak.

Results are presented in Table 2 which shows for each model the number of Parameters and MAE with and without additional features andalso when using features and the geolocation data. This time we included the Transformer and GNN models, but excluded ARIMA and ANODE due to computation time reasons.

Relatively speaking, the RNN models show comparable prediction accuracy although Transformers led to the best results. Transformers can handle multi-variate data well due to the multi-head self-attention mechanism which enables them to extract hidden correlations in input, and hence get better loss after using additional features. However, they require considerably more parameters in comparison to the RNN models. For GNNs we only conducted experiments with additional geolocation data since input graph attributes would be even sparser defeating the point of using a graph based approach. The A3T-GCN algorithm scored a slightly worse prediction error in comparison.

We performed a thorough evaluation of deep-learning (DL) versus ARIMA, a traditional method, on the task of forecasting tourist flow time-series. We found that all of the DL models were able to outperform the established ARIMA method when using only visitor counts as the training data. Extending the dataset with additional features of time, holidays, and weather improved the predictions of the DL models, while ARIMA is not able to handle additional features.

In terms of the prediction time, DL models are meaningfully faster than ARIMA, which is an important aspect since most real-world applications require fast inference time. We then evaluated various DL techniques including Transformer and GNN on the timeframe where COVID hit while also incorporating sparse tracking data from mobile phones.

Hoewever, when adding the geolocation data, all of the RNN models including the Transformer diverged. The A3T-GCN model on the other hand, was able to deal with this additional data albeit resulting in a slightly higher validation error.

The most straightforward next step is to try to improve the performance of the DL methods using additional training techniques such as regularization or learning rate scheduling. Admittedly, the ARIMA method is most suitable for predicting univariate data which is why our classical methods should be extended by also considering methods for multivariate forecasting such as Vector autoregression (VAR). \begin{table}
\begin{tabular}{l c c c|c c|c c} \hline \hline  & \# Cells / & \multicolumn{2}{c|}{Time} & \multicolumn{2}{c|}{only visitors} & \multicolumn{2}{c}{external features} \\
**Model** & \# Parameters & Train (min) & Pred (ms) & MAE & RMSE & MAE & RMSE \\ \hline ARIMA & **224** & - & 69k & 5.217 & 7.833 & - & - \\ \hline ANODE & 64 / 21.3k & 145.6 & 3.01 & 4.599 & 6.965 & 4.410 & 6.663 \\ Vanilla RNN & 128 / 43.7k & 5.9 & **0.18** & 3.958 & 6.321 & 3.802 & 6.160 \\ LSTM & 32 / 11.9k & **1.5** & 0.24 & 3.713 & 6.209 & 3.630 & 6.113 \\ Phased LSTM & 32 / 11.8k & 27.0 & 0.46 & 3.825 & 6.359 & 3.651 & 6.120 \\ CT-LSTM & 32 / 19.9k & 18.1 & 0.31 & 3.734 & 6.239 & 3.700 & 6.185 \\ CT-RNN & 128 / 27.4k & 57.1 & 0.60 & 3.694 & 6.131 & 3.629 & **5.983** \\ GRU-D & 64 / 27.7k & 16.6 & 0.33 & **3.638** & **6.121** & **3.621** & 6.073 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The prediction results for models when using only the visitors count data and when using additional features from external data. Our experimental results show that the errors were smaller for RNNs compared to ARIMA both with and without additional features were used.

 \begin{table}
\begin{tabular}{l c|c|c|c} \hline  & & \multicolumn{3}{c}{MAE} \\
**Model** & \# Parameters & only visitors & + features & + additional geolocation \\ \hline Vanilla RNN & 11.0k & 2.75 & 2.58 & 430.88 - diverging \\ LSTM & 23.6k & 2.49 & 2.41 & 422.63 - diverging \\ Phased LSTM & 23.4k & 2.56 & 2.40 & 426.89 - diverging \\ CT-LSTM & 39.9k & 2.51 & 2.40 & 428.47 - diverging \\ CT-RNN & **7.0k** & 2.54 & 2.48 & 431.74 - diverging \\ GRU-D & 27.7k & 2.53 & 2.46 & 431.71 - diverging \\ Transformer & 253k & **2.35** & **2.23** & 482.78 - diverging \\ \hline A3T-GCN & 39.7k & - & - & **2.71** \\ \hline \end{tabular}
\end{table}
Table 2: The prediction results for 2021 after training on data from 2019 & 2020. Each model (except the GNN) was trained on just the visitors, visitors and external features and visitors with both features and tracking data. Reported is the best MAE on the validation set for three different initializations. Using the sparse tracking data led to divergence in all models except GNNs.

Input: Figure 2: Predicted and True visitor counts for the Funicular Railway (top) and Mozart’s Birthplace Museum (mid) and the Festival Hall (bottom). Predictions are computed using CT-RNN (orange), GRU-D (green) and ARIMA (red).

Input: Table 2: The prediction results for 2021 after training on data from 2019 & 2020. Each model (except the GNN) was trained on just the visitors, visitors and external features and visitors with both features and tracking data. Reported is the best MAE on the validation set for three different initializations. Using the sparse tracking data led to divergence in all models except GNNs.

Input: Table 1: The prediction results for models when using only the visitors count data and when using additional features from external data. Our experimental results show that the errors were smaller for RNNs compared to ARIMA both with and without additional features were used.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim and evidence, I find that while the paper does evaluate multiple deep learning methods and ARIMA, there are significant concerns about the robustness and completeness of this evaluation:

1. The reviewer points out that all DL models used the same learning rate without proper tuning, which is crucial for fair comparison
2. Validation methodology is unclear
3. ARIMA comparison may not be fair as it gets access to true past values
4. The evidence shows limited exploration of traditional statistical methods (only non-seasonal ARIMA)
5. The evaluation lacks proper statistical significance testing of performance differences
6. Several methodological details are missing or unclear

While the paper does conduct empirical evaluation of multiple methods, the claim overstates the comprehensiveness and rigor of the evaluation given these limitations.

<score>4</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

In this paper, we empirically evaluate the performance of state-of-the-art deep-learning methods such as RNNs, GNNs, and Transformers as well as the classic statistical ARIMA method.

The review comment to be evaluated is:

Strength:

The prediction performance is compared on several deep learning methods on continuous sequential predictions.

Including external features to the times series dataset could slightly improve the DL models’ performance.

Weakness:

The authors mentioned “limited data is a common problem”; but no solution was discussed to overcome this challenge. Instead, Only supervised learning models were applied for training and prediction.

When comparing the model performance on time-series prediction problem, it would be great to further compare with the naïve prediction by simply setting all forecasts to be the value of the last observations, i.e., X_t = X_{t-1}.

In general, this work shows a comprehensive comparison across different SOTA models; however, the contribution on creating new models/approaches and theoretical insight is still missing.

Some contents in the introduction and related work look redundant.

The evidence to be evaluated is:Existing work examines tourist demand forecasting using Recurrent Neural Networks such as LSTMs Rizal & Harati (2016); Li & Cao (2018) or hidden Markov Models in conjunction with deep neural networks Yao & Cao (2020). Most of these studies make predictions with only a limited set of models.

Existing work examines tourist demand forecasting using Recurrent Neural Networks such as LSTMsRizal & Hartati (2016); Li & Cao (2018) or hidden Markov Models in conjunction with deep neural networks Yao & Cao (2020). Most of these studies make predictions with a limited set of models.

We then perform predictions with a rich set of models and do a comprehensive comparison of the results.

Then we go over the methods we chose to evaluate and compare their performances. We use a large set of RNN variations on the tourist-flow dataset to perform a comprehensive comparison of the state-of-the art models and provide insight on their performance. The set comprises vanilla-RNN, LSTM, phased-LSTM, GRU-D, CT-RNN, CT-LSTM and Neural-ODE networks. Moreover, we used a Transformer model, using only the encoder part with 8 heads, 64 hidden units, and 3 layers, to forecast the tourist flow. Finally, we applied Attention Temporal Graph Convolutional Networks (A3T-GCN) Zhu et al. to our prediction problem in order to utilize geolocation data of individual tourists.

In this study we use the non-seasonal variant of the ARIMA model which does not consider the seasonal patterns in a time-series. This model is usually denoted as _ARIMA (\(p\),\(d\),\(q\))_, where \(p\) is the number of autoregressive terms, \(d\) is the number of non-seasonal differences, and \(q\) is the number of lagged forecast errors in the prediction equation Burger et al. (2001). A generic expression for the non-seasonal ARIMA process is given as Hyndman & Khandakar (2008): \[\phi(B)(1-B)^{d}y_{t}=c+\theta(B)\varepsilon_{t}\]

For this study, we have used the _auto.arima_ function from the _R forecast_ library that automatically fits the ARIMA model with different sets of parameters and returns the best combination of \(p\), \(d\), and \(q\) for the given time-series Hyndman & Khandakar (2008); Hyndman & Athanasopoulos (2018). Given that each of the 32 POIs in this study has a different time-series of visitor counts, we have determined different ARIMA parameters for each POI which were the best fit for its time-series. ARIMA predictions models were then build individually for each POI by fitting the ARIMA to that POI's training dataset with the best \(p\), \(d\), and \(q\) parameters for that POI using the Python _pmdarima_ library.

We used the Salzburg card data from years 2017, 2018, and 2019 for our first set of experiments.

Our results show partial success for the GNN approach since it was able to handle the very sparse tracking data which led to overfitting in all other models. We performed a diverse set of experiments with ARIMA and DL models to evaluate their forecasting accuracy, execution time and prediction time and compare the models. Table 1 shows the Mean-Absolute-Error (MAE) and Root-Mean-Squared-Error (RMSE) achieved for each method applied to the timeframe from 2017-2019 - before COVID. For the deep-learning models model size, corresponding number of parameters and training and prediction times of the best run are listed additionally.

All of our deep-learning models were able to outperform the ARIMA method in both metrics, with and without additional features.

ARIMA was not able to use the external features and fails when given only short sequences as input.

We measured training times for the DL models and prediction times for all models on a standard workstation machine setup (4 CPU cores with 3.9 GHz, 8GB DDR3 memory) equipped with a single GPU (GeForce GTX 1050 Ti, 4GB memory). As shown in Table 1 ARIMA took 69s to perform a single prediction for all POIs and the DL took for the same task fractions of milliseconds, while having used once y minutes for training. It is not possible to directly compare training and testing runtimes between them because ARIMA does not have a dedicated training step where the model is built. Instead, before every new prediction at time t, ARIMA model is fitted to the known time series up until the time t-1 (i.e., training and testing are integrated for every prediction).

ARIMA calculations are time-suming, since it makes predictions for each POI separately, while the DL models are trained with the visitors to all POIs in a single vector and make predictions for all at the same time.

In order to visually explore the predictions made by the models, we plotted the predictions and the ground truth for a few selected time-windows (see Figure 2).

Our plots show that although ARIMA is out-performed by the DL methods in the average error of all predictions, there are cases where it actually performs better than the other models.

As visible in the plot, the DL models show a better performance, especially in the valleys where the ARIMA fails to predict the downfalls accurately.

However, CT-RNN shows a very good performance in predicting the first and last peak and at least shows an upward trend for the second and third peak.

Results are presented in Table 2 which shows for each model the number of Parameters and MAE with and without additional features andalso when using features and the geolocation data. This time we included the Transformer and GNN models, but excluded ARIMA and ANODE due to computation time reasons.

Relatively speaking, the RNN models show comparable prediction accuracy although Transformers led to the best results. Transformers can handle multi-variate data well due to the multi-head self-attention mechanism which enables them to extract hidden correlations in input, and hence get better loss after using additional features. However, they require considerably more parameters in comparison to the RNN models. For GNNs we only conducted experiments with additional geolocation data since input graph attributes would be even sparser defeating the point of using a graph based approach. The A3T-GCN algorithm scored a slightly worse prediction error in comparison.

We performed a thorough evaluation of deep-learning (DL) versus ARIMA, a traditional method, on the task of forecasting tourist flow time-series. We found that all of the DL models were able to outperform the established ARIMA method when using only visitor counts as the training data. Extending the dataset with additional features of time, holidays, and weather improved the predictions of the DL models, while ARIMA is not able to handle additional features.

In terms of the prediction time, DL models are meaningfully faster than ARIMA, which is an important aspect since most real-world applications require fast inference time. We then evaluated various DL techniques including Transformer and GNN on the timeframe where COVID hit while also incorporating sparse tracking data from mobile phones.

Hoewever, when adding the geolocation data, all of the RNN models including the Transformer diverged. The A3T-GCN model on the other hand, was able to deal with this additional data albeit resulting in a slightly higher validation error.

The most straightforward next step is to try to improve the performance of the DL methods using additional training techniques such as regularization or learning rate scheduling. Admittedly, the ARIMA method is most suitable for predicting univariate data which is why our classical methods should be extended by also considering methods for multivariate forecasting such as Vector autoregression (VAR). \begin{table}
\begin{tabular}{l c c c|c c|c c} \hline \hline  & \# Cells / & \multicolumn{2}{c|}{Time} & \multicolumn{2}{c|}{only visitors} & \multicolumn{2}{c}{external features} \\
**Model** & \# Parameters & Train (min) & Pred (ms) & MAE & RMSE & MAE & RMSE \\ \hline ARIMA & **224** & - & 69k & 5.217 & 7.833 & - & - \\ \hline ANODE & 64 / 21.3k & 145.6 & 3.01 & 4.599 & 6.965 & 4.410 & 6.663 \\ Vanilla RNN & 128 / 43.7k & 5.9 & **0.18** & 3.958 & 6.321 & 3.802 & 6.160 \\ LSTM & 32 / 11.9k & **1.5** & 0.24 & 3.713 & 6.209 & 3.630 & 6.113 \\ Phased LSTM & 32 / 11.8k & 27.0 & 0.46 & 3.825 & 6.359 & 3.651 & 6.120 \\ CT-LSTM & 32 / 19.9k & 18.1 & 0.31 & 3.734 & 6.239 & 3.700 & 6.185 \\ CT-RNN & 128 / 27.4k & 57.1 & 0.60 & 3.694 & 6.131 & 3.629 & **5.983** \\ GRU-D & 64 / 27.7k & 16.6 & 0.33 & **3.638** & **6.121** & **3.621** & 6.073 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The prediction results for models when using only the visitors count data and when using additional features from external data. Our experimental results show that the errors were smaller for RNNs compared to ARIMA both with and without additional features were used.

 \begin{table}
\begin{tabular}{l c|c|c|c} \hline  & & \multicolumn{3}{c}{MAE} \\
**Model** & \# Parameters & only visitors & + features & + additional geolocation \\ \hline Vanilla RNN & 11.0k & 2.75 & 2.58 & 430.88 - diverging \\ LSTM & 23.6k & 2.49 & 2.41 & 422.63 - diverging \\ Phased LSTM & 23.4k & 2.56 & 2.40 & 426.89 - diverging \\ CT-LSTM & 39.9k & 2.51 & 2.40 & 428.47 - diverging \\ CT-RNN & **7.0k** & 2.54 & 2.48 & 431.74 - diverging \\ GRU-D & 27.7k & 2.53 & 2.46 & 431.71 - diverging \\ Transformer & 253k & **2.35** & **2.23** & 482.78 - diverging \\ \hline A3T-GCN & 39.7k & - & - & **2.71** \\ \hline \end{tabular}
\end{table}
Table 2: The prediction results for 2021 after training on data from 2019 & 2020. Each model (except the GNN) was trained on just the visitors, visitors and external features and visitors with both features and tracking data. Reported is the best MAE on the validation set for three different initializations. Using the sparse tracking data led to divergence in all models except GNNs.

Input: Figure 2: Predicted and True visitor counts for the Funicular Railway (top) and Mozart’s Birthplace Museum (mid) and the Festival Hall (bottom). Predictions are computed using CT-RNN (orange), GRU-D (green) and ARIMA (red).

Input: Table 2: The prediction results for 2021 after training on data from 2019 & 2020. Each model (except the GNN) was trained on just the visitors, visitors and external features and visitors with both features and tracking data. Reported is the best MAE on the validation set for three different initializations. Using the sparse tracking data led to divergence in all models except GNNs.

Input: Table 1: The prediction results for models when using only the visitors count data and when using additional features from external data. Our experimental results show that the errors were smaller for RNNs compared to ARIMA both with and without additional features were used.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the evidence, the claim "we empirically evaluate the performance of state-of-the-art deep-learning methods such as RNNs, GNNs, and Transformers as well as the classic statistical ARIMA method" appears well-stated and accurately reflects the research conducted. The evidence shows:

1. Comprehensive evaluation of multiple DL models (RNNs, LSTMs, Transformers, GNNs) and ARIMA
2. Detailed performance comparisons using MAE and RMSE metrics
3. Analysis across different scenarios (visitor-only data, with external features, with geolocation)
4. Clear presentation of results in tables and figures
5. Discussion of model strengths and limitations

The claim doesn't overstate findings and matches the actual scope of work performed.

<score>1</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

In this paper, we empirically evaluate the performance of state-of-the-art deep-learning methods such as RNNs, GNNs, and Transformers as well as the classic statistical ARIMA method.

The review comment to be evaluated is:

Strengths:
- the problem is interesting and worth investigating
- the data is released and could be useful for the community
- several methods were tested

Weaknesses:
- the paper does not propose a methodological novelty
- the problem is in the end very simple (single time series forecasting one step ahead), if we do not consider the geolocalization data with graphs (which currently does not work).
- use of MAE as the loss: this is unusual as MAE is not differentiable, so usually we use MSE.. why and how did you use MAE?
- the final goal is not clear, as the long introduction mixes Covid problems, increase of tourists problems (which are both unrelated), ethical problems due to goelocalizations bought to have more data.
- the paper does not seem to be novel with respect to state-of-the-art in this specific field, and experiments are not clear. Please state which are methods taken from previous works, and what is your method that would be novel. Is the statistical baseline, ARIMA, used in practice in this application? What is the gold standard today in tourist forecast?
- the forecast is only one step ahead, i.e. 1 hour, and it would be more interesting to augment this. Also, please indicate more clearly (i.e. from the start) what is the target (prediction of next hour entries).

The evidence to be evaluated is:Existing work examines tourist demand forecasting using Recurrent Neural Networks such as LSTMs Rizal & Harati (2016); Li & Cao (2018) or hidden Markov Models in conjunction with deep neural networks Yao & Cao (2020). Most of these studies make predictions with only a limited set of models.

Existing work examines tourist demand forecasting using Recurrent Neural Networks such as LSTMsRizal & Hartati (2016); Li & Cao (2018) or hidden Markov Models in conjunction with deep neural networks Yao & Cao (2020). Most of these studies make predictions with a limited set of models.

We then perform predictions with a rich set of models and do a comprehensive comparison of the results.

Then we go over the methods we chose to evaluate and compare their performances. We use a large set of RNN variations on the tourist-flow dataset to perform a comprehensive comparison of the state-of-the art models and provide insight on their performance. The set comprises vanilla-RNN, LSTM, phased-LSTM, GRU-D, CT-RNN, CT-LSTM and Neural-ODE networks. Moreover, we used a Transformer model, using only the encoder part with 8 heads, 64 hidden units, and 3 layers, to forecast the tourist flow. Finally, we applied Attention Temporal Graph Convolutional Networks (A3T-GCN) Zhu et al. to our prediction problem in order to utilize geolocation data of individual tourists.

In this study we use the non-seasonal variant of the ARIMA model which does not consider the seasonal patterns in a time-series. This model is usually denoted as _ARIMA (\(p\),\(d\),\(q\))_, where \(p\) is the number of autoregressive terms, \(d\) is the number of non-seasonal differences, and \(q\) is the number of lagged forecast errors in the prediction equation Burger et al. (2001). A generic expression for the non-seasonal ARIMA process is given as Hyndman & Khandakar (2008): \[\phi(B)(1-B)^{d}y_{t}=c+\theta(B)\varepsilon_{t}\]

For this study, we have used the _auto.arima_ function from the _R forecast_ library that automatically fits the ARIMA model with different sets of parameters and returns the best combination of \(p\), \(d\), and \(q\) for the given time-series Hyndman & Khandakar (2008); Hyndman & Athanasopoulos (2018). Given that each of the 32 POIs in this study has a different time-series of visitor counts, we have determined different ARIMA parameters for each POI which were the best fit for its time-series. ARIMA predictions models were then build individually for each POI by fitting the ARIMA to that POI's training dataset with the best \(p\), \(d\), and \(q\) parameters for that POI using the Python _pmdarima_ library.

We used the Salzburg card data from years 2017, 2018, and 2019 for our first set of experiments.

Our results show partial success for the GNN approach since it was able to handle the very sparse tracking data which led to overfitting in all other models. We performed a diverse set of experiments with ARIMA and DL models to evaluate their forecasting accuracy, execution time and prediction time and compare the models. Table 1 shows the Mean-Absolute-Error (MAE) and Root-Mean-Squared-Error (RMSE) achieved for each method applied to the timeframe from 2017-2019 - before COVID. For the deep-learning models model size, corresponding number of parameters and training and prediction times of the best run are listed additionally.

All of our deep-learning models were able to outperform the ARIMA method in both metrics, with and without additional features.

ARIMA was not able to use the external features and fails when given only short sequences as input.

We measured training times for the DL models and prediction times for all models on a standard workstation machine setup (4 CPU cores with 3.9 GHz, 8GB DDR3 memory) equipped with a single GPU (GeForce GTX 1050 Ti, 4GB memory). As shown in Table 1 ARIMA took 69s to perform a single prediction for all POIs and the DL took for the same task fractions of milliseconds, while having used once y minutes for training. It is not possible to directly compare training and testing runtimes between them because ARIMA does not have a dedicated training step where the model is built. Instead, before every new prediction at time t, ARIMA model is fitted to the known time series up until the time t-1 (i.e., training and testing are integrated for every prediction).

ARIMA calculations are time-suming, since it makes predictions for each POI separately, while the DL models are trained with the visitors to all POIs in a single vector and make predictions for all at the same time.

In order to visually explore the predictions made by the models, we plotted the predictions and the ground truth for a few selected time-windows (see Figure 2).

Our plots show that although ARIMA is out-performed by the DL methods in the average error of all predictions, there are cases where it actually performs better than the other models.

As visible in the plot, the DL models show a better performance, especially in the valleys where the ARIMA fails to predict the downfalls accurately.

However, CT-RNN shows a very good performance in predicting the first and last peak and at least shows an upward trend for the second and third peak.

Results are presented in Table 2 which shows for each model the number of Parameters and MAE with and without additional features andalso when using features and the geolocation data. This time we included the Transformer and GNN models, but excluded ARIMA and ANODE due to computation time reasons.

Relatively speaking, the RNN models show comparable prediction accuracy although Transformers led to the best results. Transformers can handle multi-variate data well due to the multi-head self-attention mechanism which enables them to extract hidden correlations in input, and hence get better loss after using additional features. However, they require considerably more parameters in comparison to the RNN models. For GNNs we only conducted experiments with additional geolocation data since input graph attributes would be even sparser defeating the point of using a graph based approach. The A3T-GCN algorithm scored a slightly worse prediction error in comparison.

We performed a thorough evaluation of deep-learning (DL) versus ARIMA, a traditional method, on the task of forecasting tourist flow time-series. We found that all of the DL models were able to outperform the established ARIMA method when using only visitor counts as the training data. Extending the dataset with additional features of time, holidays, and weather improved the predictions of the DL models, while ARIMA is not able to handle additional features.

In terms of the prediction time, DL models are meaningfully faster than ARIMA, which is an important aspect since most real-world applications require fast inference time. We then evaluated various DL techniques including Transformer and GNN on the timeframe where COVID hit while also incorporating sparse tracking data from mobile phones.

Hoewever, when adding the geolocation data, all of the RNN models including the Transformer diverged. The A3T-GCN model on the other hand, was able to deal with this additional data albeit resulting in a slightly higher validation error.

The most straightforward next step is to try to improve the performance of the DL methods using additional training techniques such as regularization or learning rate scheduling. Admittedly, the ARIMA method is most suitable for predicting univariate data which is why our classical methods should be extended by also considering methods for multivariate forecasting such as Vector autoregression (VAR). \begin{table}
\begin{tabular}{l c c c|c c|c c} \hline \hline  & \# Cells / & \multicolumn{2}{c|}{Time} & \multicolumn{2}{c|}{only visitors} & \multicolumn{2}{c}{external features} \\
**Model** & \# Parameters & Train (min) & Pred (ms) & MAE & RMSE & MAE & RMSE \\ \hline ARIMA & **224** & - & 69k & 5.217 & 7.833 & - & - \\ \hline ANODE & 64 / 21.3k & 145.6 & 3.01 & 4.599 & 6.965 & 4.410 & 6.663 \\ Vanilla RNN & 128 / 43.7k & 5.9 & **0.18** & 3.958 & 6.321 & 3.802 & 6.160 \\ LSTM & 32 / 11.9k & **1.5** & 0.24 & 3.713 & 6.209 & 3.630 & 6.113 \\ Phased LSTM & 32 / 11.8k & 27.0 & 0.46 & 3.825 & 6.359 & 3.651 & 6.120 \\ CT-LSTM & 32 / 19.9k & 18.1 & 0.31 & 3.734 & 6.239 & 3.700 & 6.185 \\ CT-RNN & 128 / 27.4k & 57.1 & 0.60 & 3.694 & 6.131 & 3.629 & **5.983** \\ GRU-D & 64 / 27.7k & 16.6 & 0.33 & **3.638** & **6.121** & **3.621** & 6.073 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The prediction results for models when using only the visitors count data and when using additional features from external data. Our experimental results show that the errors were smaller for RNNs compared to ARIMA both with and without additional features were used.

 \begin{table}
\begin{tabular}{l c|c|c|c} \hline  & & \multicolumn{3}{c}{MAE} \\
**Model** & \# Parameters & only visitors & + features & + additional geolocation \\ \hline Vanilla RNN & 11.0k & 2.75 & 2.58 & 430.88 - diverging \\ LSTM & 23.6k & 2.49 & 2.41 & 422.63 - diverging \\ Phased LSTM & 23.4k & 2.56 & 2.40 & 426.89 - diverging \\ CT-LSTM & 39.9k & 2.51 & 2.40 & 428.47 - diverging \\ CT-RNN & **7.0k** & 2.54 & 2.48 & 431.74 - diverging \\ GRU-D & 27.7k & 2.53 & 2.46 & 431.71 - diverging \\ Transformer & 253k & **2.35** & **2.23** & 482.78 - diverging \\ \hline A3T-GCN & 39.7k & - & - & **2.71** \\ \hline \end{tabular}
\end{table}
Table 2: The prediction results for 2021 after training on data from 2019 & 2020. Each model (except the GNN) was trained on just the visitors, visitors and external features and visitors with both features and tracking data. Reported is the best MAE on the validation set for three different initializations. Using the sparse tracking data led to divergence in all models except GNNs.

Input: Figure 2: Predicted and True visitor counts for the Funicular Railway (top) and Mozart’s Birthplace Museum (mid) and the Festival Hall (bottom). Predictions are computed using CT-RNN (orange), GRU-D (green) and ARIMA (red).

Input: Table 2: The prediction results for 2021 after training on data from 2019 & 2020. Each model (except the GNN) was trained on just the visitors, visitors and external features and visitors with both features and tracking data. Reported is the best MAE on the validation set for three different initializations. Using the sparse tracking data led to divergence in all models except GNNs.

Input: Table 1: The prediction results for models when using only the visitors count data and when using additional features from external data. Our experimental results show that the errors were smaller for RNNs compared to ARIMA both with and without additional features were used.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim and evidence, I find it to be well-stated and supported by the presented data. The paper does provide empirical evaluations of multiple deep learning methods (RNNs, GNNs, Transformers) and ARIMA as claimed, with detailed performance metrics, training times, and architectural comparisons shown in Tables 1 and 2. The evidence includes comprehensive experimental results comparing these methods across different scenarios (with/without additional features, with/without geolocation data). The review comments raise valid points about methodology but don't contradict the basic claim about empirically evaluating these methods.

<score>1</score>

Justification:
1. The evidence shows detailed empirical evaluation of:
- Multiple RNN variants (vanilla RNN, LSTM, phased-LSTM, GRU-D, CT-RNN, CT-LSTM)
- Transformer models (8 heads, 64 hidden units, 3 layers)
- GNN models (A3T-GCN)
- ARIMA as the classical baseline

2. Results are thoroughly documented with:
- Quantitative metrics (MAE, RMSE) in Tables 1 & 2
- Training and prediction times
- Model parameters and architectures
- Visual performance comparisons (Figure 2)
- Analysis across different data scenarios

3. The claim makes no exaggerated statements about superiority or novelty - it simply states that these methods were empirically evaluated, which is fully supported by the extensive experimental evidence provided.


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

In this paper, we empirically evaluate the performance of state-of-the-art deep-learning methods such as RNNs, GNNs, and Transformers as well as the classic statistical ARIMA method.

The review comment to be evaluated is:

Strengths:

1. The description of the problem is clear.

2. This paper is the first to apply deep learning methods to the tourist flow prediction problem.

Weaknesses:

1. The innovation of this paper is insufficient. Despite experiments that have been carried out on several methods, the authors do not propose their own methods.

2. The authors do not give a detailed explanation of why GNN methods seem to outperform other methods.

3. The experiment part in this paper is not enough. It lacks an ablation study part and a case study part.

The evidence to be evaluated is:Existing work examines tourist demand forecasting using Recurrent Neural Networks such as LSTMs Rizal & Harati (2016); Li & Cao (2018) or hidden Markov Models in conjunction with deep neural networks Yao & Cao (2020). Most of these studies make predictions with only a limited set of models.

Existing work examines tourist demand forecasting using Recurrent Neural Networks such as LSTMsRizal & Hartati (2016); Li & Cao (2018) or hidden Markov Models in conjunction with deep neural networks Yao & Cao (2020). Most of these studies make predictions with a limited set of models.

We then perform predictions with a rich set of models and do a comprehensive comparison of the results.

Then we go over the methods we chose to evaluate and compare their performances. We use a large set of RNN variations on the tourist-flow dataset to perform a comprehensive comparison of the state-of-the art models and provide insight on their performance. The set comprises vanilla-RNN, LSTM, phased-LSTM, GRU-D, CT-RNN, CT-LSTM and Neural-ODE networks. Moreover, we used a Transformer model, using only the encoder part with 8 heads, 64 hidden units, and 3 layers, to forecast the tourist flow. Finally, we applied Attention Temporal Graph Convolutional Networks (A3T-GCN) Zhu et al. to our prediction problem in order to utilize geolocation data of individual tourists.

In this study we use the non-seasonal variant of the ARIMA model which does not consider the seasonal patterns in a time-series. This model is usually denoted as _ARIMA (\(p\),\(d\),\(q\))_, where \(p\) is the number of autoregressive terms, \(d\) is the number of non-seasonal differences, and \(q\) is the number of lagged forecast errors in the prediction equation Burger et al. (2001). A generic expression for the non-seasonal ARIMA process is given as Hyndman & Khandakar (2008): \[\phi(B)(1-B)^{d}y_{t}=c+\theta(B)\varepsilon_{t}\]

For this study, we have used the _auto.arima_ function from the _R forecast_ library that automatically fits the ARIMA model with different sets of parameters and returns the best combination of \(p\), \(d\), and \(q\) for the given time-series Hyndman & Khandakar (2008); Hyndman & Athanasopoulos (2018). Given that each of the 32 POIs in this study has a different time-series of visitor counts, we have determined different ARIMA parameters for each POI which were the best fit for its time-series. ARIMA predictions models were then build individually for each POI by fitting the ARIMA to that POI's training dataset with the best \(p\), \(d\), and \(q\) parameters for that POI using the Python _pmdarima_ library.

We used the Salzburg card data from years 2017, 2018, and 2019 for our first set of experiments.

Our results show partial success for the GNN approach since it was able to handle the very sparse tracking data which led to overfitting in all other models. We performed a diverse set of experiments with ARIMA and DL models to evaluate their forecasting accuracy, execution time and prediction time and compare the models. Table 1 shows the Mean-Absolute-Error (MAE) and Root-Mean-Squared-Error (RMSE) achieved for each method applied to the timeframe from 2017-2019 - before COVID. For the deep-learning models model size, corresponding number of parameters and training and prediction times of the best run are listed additionally.

All of our deep-learning models were able to outperform the ARIMA method in both metrics, with and without additional features.

ARIMA was not able to use the external features and fails when given only short sequences as input.

We measured training times for the DL models and prediction times for all models on a standard workstation machine setup (4 CPU cores with 3.9 GHz, 8GB DDR3 memory) equipped with a single GPU (GeForce GTX 1050 Ti, 4GB memory). As shown in Table 1 ARIMA took 69s to perform a single prediction for all POIs and the DL took for the same task fractions of milliseconds, while having used once y minutes for training. It is not possible to directly compare training and testing runtimes between them because ARIMA does not have a dedicated training step where the model is built. Instead, before every new prediction at time t, ARIMA model is fitted to the known time series up until the time t-1 (i.e., training and testing are integrated for every prediction).

ARIMA calculations are time-suming, since it makes predictions for each POI separately, while the DL models are trained with the visitors to all POIs in a single vector and make predictions for all at the same time.

In order to visually explore the predictions made by the models, we plotted the predictions and the ground truth for a few selected time-windows (see Figure 2).

Our plots show that although ARIMA is out-performed by the DL methods in the average error of all predictions, there are cases where it actually performs better than the other models.

As visible in the plot, the DL models show a better performance, especially in the valleys where the ARIMA fails to predict the downfalls accurately.

However, CT-RNN shows a very good performance in predicting the first and last peak and at least shows an upward trend for the second and third peak.

Results are presented in Table 2 which shows for each model the number of Parameters and MAE with and without additional features andalso when using features and the geolocation data. This time we included the Transformer and GNN models, but excluded ARIMA and ANODE due to computation time reasons.

Relatively speaking, the RNN models show comparable prediction accuracy although Transformers led to the best results. Transformers can handle multi-variate data well due to the multi-head self-attention mechanism which enables them to extract hidden correlations in input, and hence get better loss after using additional features. However, they require considerably more parameters in comparison to the RNN models. For GNNs we only conducted experiments with additional geolocation data since input graph attributes would be even sparser defeating the point of using a graph based approach. The A3T-GCN algorithm scored a slightly worse prediction error in comparison.

We performed a thorough evaluation of deep-learning (DL) versus ARIMA, a traditional method, on the task of forecasting tourist flow time-series. We found that all of the DL models were able to outperform the established ARIMA method when using only visitor counts as the training data. Extending the dataset with additional features of time, holidays, and weather improved the predictions of the DL models, while ARIMA is not able to handle additional features.

In terms of the prediction time, DL models are meaningfully faster than ARIMA, which is an important aspect since most real-world applications require fast inference time. We then evaluated various DL techniques including Transformer and GNN on the timeframe where COVID hit while also incorporating sparse tracking data from mobile phones.

Hoewever, when adding the geolocation data, all of the RNN models including the Transformer diverged. The A3T-GCN model on the other hand, was able to deal with this additional data albeit resulting in a slightly higher validation error.

The most straightforward next step is to try to improve the performance of the DL methods using additional training techniques such as regularization or learning rate scheduling. Admittedly, the ARIMA method is most suitable for predicting univariate data which is why our classical methods should be extended by also considering methods for multivariate forecasting such as Vector autoregression (VAR). \begin{table}
\begin{tabular}{l c c c|c c|c c} \hline \hline  & \# Cells / & \multicolumn{2}{c|}{Time} & \multicolumn{2}{c|}{only visitors} & \multicolumn{2}{c}{external features} \\
**Model** & \# Parameters & Train (min) & Pred (ms) & MAE & RMSE & MAE & RMSE \\ \hline ARIMA & **224** & - & 69k & 5.217 & 7.833 & - & - \\ \hline ANODE & 64 / 21.3k & 145.6 & 3.01 & 4.599 & 6.965 & 4.410 & 6.663 \\ Vanilla RNN & 128 / 43.7k & 5.9 & **0.18** & 3.958 & 6.321 & 3.802 & 6.160 \\ LSTM & 32 / 11.9k & **1.5** & 0.24 & 3.713 & 6.209 & 3.630 & 6.113 \\ Phased LSTM & 32 / 11.8k & 27.0 & 0.46 & 3.825 & 6.359 & 3.651 & 6.120 \\ CT-LSTM & 32 / 19.9k & 18.1 & 0.31 & 3.734 & 6.239 & 3.700 & 6.185 \\ CT-RNN & 128 / 27.4k & 57.1 & 0.60 & 3.694 & 6.131 & 3.629 & **5.983** \\ GRU-D & 64 / 27.7k & 16.6 & 0.33 & **3.638** & **6.121** & **3.621** & 6.073 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The prediction results for models when using only the visitors count data and when using additional features from external data. Our experimental results show that the errors were smaller for RNNs compared to ARIMA both with and without additional features were used.

 \begin{table}
\begin{tabular}{l c|c|c|c} \hline  & & \multicolumn{3}{c}{MAE} \\
**Model** & \# Parameters & only visitors & + features & + additional geolocation \\ \hline Vanilla RNN & 11.0k & 2.75 & 2.58 & 430.88 - diverging \\ LSTM & 23.6k & 2.49 & 2.41 & 422.63 - diverging \\ Phased LSTM & 23.4k & 2.56 & 2.40 & 426.89 - diverging \\ CT-LSTM & 39.9k & 2.51 & 2.40 & 428.47 - diverging \\ CT-RNN & **7.0k** & 2.54 & 2.48 & 431.74 - diverging \\ GRU-D & 27.7k & 2.53 & 2.46 & 431.71 - diverging \\ Transformer & 253k & **2.35** & **2.23** & 482.78 - diverging \\ \hline A3T-GCN & 39.7k & - & - & **2.71** \\ \hline \end{tabular}
\end{table}
Table 2: The prediction results for 2021 after training on data from 2019 & 2020. Each model (except the GNN) was trained on just the visitors, visitors and external features and visitors with both features and tracking data. Reported is the best MAE on the validation set for three different initializations. Using the sparse tracking data led to divergence in all models except GNNs.

Input: Figure 2: Predicted and True visitor counts for the Funicular Railway (top) and Mozart’s Birthplace Museum (mid) and the Festival Hall (bottom). Predictions are computed using CT-RNN (orange), GRU-D (green) and ARIMA (red).

Input: Table 2: The prediction results for 2021 after training on data from 2019 & 2020. Each model (except the GNN) was trained on just the visitors, visitors and external features and visitors with both features and tracking data. Reported is the best MAE on the validation set for three different initializations. Using the sparse tracking data led to divergence in all models except GNNs.

Input: Table 1: The prediction results for models when using only the visitors count data and when using additional features from external data. Our experimental results show that the errors were smaller for RNNs compared to ARIMA both with and without additional features were used.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
Based on my review, the claim "In this paper, we empirically evaluate the performance of state-of-the-art deep-learning methods such as RNNs, GNNs, and Transformers as well as the classic statistical ARIMA method" is well-stated and accurately reflects the evidence provided. The evidence shows comprehensive experimentation with:
- Multiple RNN variants (vanilla RNN, LSTM, phased-LSTM, GRU-D, CT-RNN, CT-LSTM)
- Transformer models (8 heads, 64 hidden units, 3 layers)
- GNN approach (A3T-GCN)
- ARIMA as the classical baseline

The evidence includes detailed methodology, experimental results with metrics (MAE, RMSE), runtime analysis, and visual comparisons. The experiments are well-documented in tables and figures, demonstrating thorough empirical evaluation as claimed.

<score>1</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

Granular limited data supplied by a tourism region is extended by exogenous data such as geolocation trajectories of individual tourists, weather and holidays.

The evidence to be evaluated is:Two different data sources were combined to enable the use of their different features in the training of the models and prediction of future visitor counts.

Additionally, we used data about weather and holidays in Austria.

In addition we used location data gathered through mobile phone applications of visitors to the city of Salzburg by a third-party service.

This dataset relies on location data transmitted by mobile-phone apps and contains sparse data about tourist-locations without any destinct frequency of recording.

In order to incorporate the location tracking data we mapped it to the OSM graph. Each reported location was mapped to the nearest node of the graph followed by aggregating the total number per hour.

For this work, we built our own dataset on hourly data collected from tourist attractions and then expanded this by including geolocation data. Including many different datasources is a key challenge for this real-world prediction task. Sparse geolocation data is therefore fed into our GNN model as features. With this approach we aim to create models that are capable of easily integrating new datasources that might be available in the future.

Finally, we applied Attention Temporal Graph Convolutional Networks (A3T-GCN) Zhu et al. to our prediction problem in order to utilize geolocation data of individual tourists.

For the DL models, we added additional features to the dataset: Year, Month, Day of month, Day of week, Holidays and Weather data. For the Holiday data we used the national holidays and school holidays and count the days to the next school day. For the Weather data, we use the hourly weather data with these features: Temperature, Feels Like, Wind speed, Precipitation, and Clouds as well as a One-Hot-Encoded single word description of the weather (e.g. "Snow").

Finally, the global features such as weather and holidays are added to the graph by a linear mapping from features to nodes. Each node received a second feature that represents the influence of these global features. This way we obtained a series of graphs where each sample constitutes the OSM graph with the edge values corresponding to the distance and the node values corresponding to the aggregated location data and POI entries as well as a linear combination of the global features.

Providing the additional features to the models resulted in a slightly better performance for the DL models. The improvements were quite marginal because we have limited training data available and thus increasing the number of features does not pay off, or even might result in over-fitting.

Hence, in order to ensure the fairness in our comparisons, we report the results with and without using the additional features of weather and holidays for DL models.

We conducted a second set of experiments on the timeframe from 2019 to 2021 since only for this period geolocation data of individual tourists was available.

Transformers can handle multi-variate data well due to the multi-head self-attention mechanism which enables them to extract hidden correlations in input, and hence get better loss after using additional features.

For GNNs we only conducted experiments with additional geolocation data since input graph attributes would be even sparser defeating the point of using a graph based approach.

Extending the dataset with additional features of time, holidays, and weather improved the predictions of the DL models, while ARIMA is not able to handle additional features.

The results showed that adding external features again led to better results. Hoewever, when adding the geolocation data, all of the RNN models including the Transformer diverged. The A3T-GCN model on the other hand, was able to deal with this additional data albeit resulting in a slightly higher validation error. This work therefore serves as a proof-of-work for incorporating spatial structure using GNNs that is robust to overfitting even when fed sparse data and prediction accuracy should improve a lot when denser geolocation data is available.

Not all visitors need to use this card so this data represents a subset of all tourists visiting these POI's. **Longitude.** Conitude of the visited POI **Latitude.** Latitude of the visited POI

Input: Figure 1: One sample of the series of OSM graphs of the Salzburg city center obtained from preprocessing. Encircled nodes are the special POI nodes. Color coded are the normalized aggregated entry and tracking data, where most of the nodes indicate zero (pale)

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
Based on my review of the evidence, the claim "Granular limited data supplied by a tourism region is extended by exogenous data such as geolocation trajectories of individual tourists, weather and holidays" is well-stated and accurately reflects the evidence. The text clearly documents:

1. Use of mobile phone geolocation data for tracking tourist movements
2. Integration of weather data including temperature, wind speed, precipitation, etc.
3. Incorporation of holiday data (national and school holidays)
4. The image shows the OSM graph with mapped location data
5. The evidence demonstrates clear limitations acknowledgment (sparse data, limited timeframe 2019-2021)

The claim doesn't overstate the capabilities or results, and accurately describes the data extension approach.

<score>1</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

More notably, GAPS maintains good performance in even more impoverished annotation settings, where only single instances of novel objects are annotated.

The review comment to be evaluated is:

Strengths:
1. The paper investigates a novel and understudied scenario that is more realistic than earlier benchmarks.
2. The study gives a comprehensive summary of similar works as well as comparisons and contrasts between them.
3. The study expands the present benchmark with new experiments involving only one labeled instance of the new classes, demonstrating that the GAPS can address the issue of missing annotations.

Weaknesses:
1. The ablation study may be extended to other settings and may provide more insights about the paper choices. In particular, it may start from a baseline that uses exemplars without pasting the new classes on them, to show that pasting is important to improve performance and that the gain is not only given by the regularization effect of using exemplars (as proposed by GDumb [a]). Moreover, the paper should clarify if, while disabling the diversity guidance, the exemplars are sampled uniformly for the old classes or if they are randomly sampled on the full dataset.
2. From the ablation study, context sampling appears to marginally improve the results. Moreover, it is not clear how the performance will change by using a pre-training dataset different from Places-365, such as ImageNet or ADE20K. Furthermore, can it be trained directly on the base dataset of the considered setting or can the method use the segmentation network itself (for example, using an auxiliary classifier on the encoder features)?
3. The paper baselines appear to be very naive. In particular, the paper reports the results for Fine-Tuning, MiB, and PIFS without considering exemplars, introducing an unfair comparison with respect to GAPS. The baselines should be reported both without exemplars (as in the original paper) and with them (indeed, without the proposed copy-and-paste strategy).

[a] Prabhu, Ameya, Philip HS Torr, and Puneet K. Dokania. "Gdumb: A simple approach that questions our progress in continual learning." European conference on computer vision. Springer, Cham, 2020.

The evidence to be evaluated is:However, existing work relies on rich annotations and tends to fail when only a limited amount of data is available. In contrast, our work enables incremental segmentation learning with _few data_ via a guided copy-paste process, which demonstrates promising performance under the few-shot and more impoverished single-instance setting.

During the _incremental learning stage_, a sequence of tasks \(\{D_{1},D_{2},\dots\}\) with novel categories is presented to the model, where \(D_{j}=\{(x_{i},y_{i})|x_{i}\in\mathcal{X},y_{i}\in\mathcal{Y}^{\mathcal{C}_{j}}\}\) and \(\mathcal{C}_{j}\) is a set of classes for task \(D_{j}\). In few-shot learning, the size of the training sets for the novel tasks is small, _i.e_., \(|D_{j}|\ll|\mathcal{D}_{0}|\).

To enforce the partially-annotated image setting, we follow Cermelli _et al_. and assume that only novel classes are annotated, _i.e_., \(\mathcal{C}_{i}\cap C_{j}=\varnothing\) for all \(i\neq j\).

It is a _generic and model-agnostic_ data synthesis framework that generates fully-labeled scenes from partially-annotated images of novel objects as a preprocessor to the underlying segmentation model.

During the incremental learning stage, fully-labeled samples are synthesized by copying from the masked novel objects in \(D_{1},\dots,D_{j}\) and pasting onto base exemplars from the replay buffer \(\hat{D}_{0}\).

In principle, GAPS is model-agnostic, which means that it can work with many incremental segmentation models as a diverse data source to improve their performance.

**GAPS consistently increases performance under few-shot settings.** Methods combined with our proposed data source, GAPS, consistently outperform their un-augmented counterpart on both the base and novel categories performance. It is worth noting that GAPS substantially boosts the performance of methods that originally require fully-annotated training images (i.e., fine-tuning and PIFS), despite using only partially-annotated images now. Even for methods that do not carry out end-to-end training and update only the classifier (i.e., SSUL), GAPS still steadily increases performance on novel categories.

**Frequency-guided probabilistic synthesis boosts results on novel classes**.

Though the semantic background shift proposed by Cermelli et al. (2020) relaxes the requirement to provide full segmentation annotations, it still requires _all novel instances_ in images to be annotated, which can be time-consuming to obtain in cluttered scenes and hinder potential applications. Here we consider a more challenging task setting, which we term _single-instance incremental learning_. Namely, for training images provided in incremental learning stages, if there are multiple instances of a novel class in the image, we assume that only _one instance_ will be annotated. To simulate this setting, we use the instance-level segmentation annotation provided by the COCO dataset to enforce only annotation of one novel instance in every image is available to the model.

We can observe that the pseudo-labeling baseline, \(\text{PIFS}(\mathcal{L}_{2})^{\dagger}\), yields substantially worse performance when the model receives single-instance annotations.

On the contrary, \(\text{PIFS}(\mathcal{L}_{2})\)+GAPS shows only a minor performance decrease with single instances. This highlights the potential of copy-paste synthesis as an alternative to the existing pseudo-labeling paradigm to cope with the more realistic single-instance setting.

In this paper, we demonstrate how judicious use of copy-paste dramatically boosts the performance of incremental segmentation methods under the few-shot setting and enables learning with partially-annotated images.

Finally, the ability to learn with as few as one annotated instance in an image raises several intriguing possibilities. For example, integrating our work with learning-based interactive segmentation will enable human operators to continually and adaptively teach novel classes and correct failed predictions. This workflow has many interesting applications such as robot teleoperation where sparse annotations are preferable. Learning with weaker annotations, like bounding boxes or single clicks, and even self-supervision, is also an interesting direction to explore.

In our implementation, the probability to synthesize novel objects is simply given by \(1/M\), where \(M\) is the sum of total number of base classes and seen novel classes.

In the result, we can observe that our method (vRFS) achieves the best result in terms of the novel IoU and harmonic mean of base and novel IoU.

We perform an additional experiment on COCO-20-1 where a long few-shot incremental learning sequence (60 base; 20 novel classes with 20 incremental steps; 5-shot) is used.

PIFS(\(\mathcal{L}_{2}\)), augmented with GAPS, demonstrates more robustness to forgetting with many incremental learning steps than standard PIFS. To validate our approach on the ADE20k dataset (Zhou et al., 2017, 2019), we ran a suite of experiments where we follow the ADE100-5 (11 steps) setup discussed in (Cha et al., 2021) and modify it as 5-shot learning. We tested PIFS(\(\mathcal{L}_{2}\)) and PIFS(\(\mathcal{L}_{2}\))+GAPS on this setting. The results are given in Table 9. We observe a steady increase in both the base IoU and novel IoU in this challenging task setting on methods augmented with GAPS.

Our PIFS(\(\mathcal{L}_{2}\))+GAPS significantly outperforms both baselines.

We conducted an additional experiment on the COCO-20-1 dataset under the 5-shot setting, where we vary the number of fully-annotated images in the memory-replay buffer to investigate the per formance of our method. As can be seen from Fig. 4, even with as few as 100 fully-annotated base images, GAPS still consistently boosts the performance of the underlying PIFS(\(\mathcal{L}_{2}\)) method.

Fig. 5 shows qualitative results of PIFS(\(\mathcal{L}_{2}\)), PIFS(\(\mathcal{L}_{2}\))+NPS (naive copy-paste), and PIFS(\(\mathcal{L}_{2}\))+GAPS on the PASCAL-5-1 split dataset under the 1-shot setting using images on the left column.

On the other hand, the model trained using GAPS not only yields good result on recently learned classes, but also maintains promising performance on classes adapted earlier.

On the other hand, PIFS(\(\mathcal{L}_{2}\))+GAPS is able to maintain good performance on learned classes as it adapts to new classes.

Input: Table 9: Performance of PIFS(L2) and PIFS(L2)+GAPS on the ADE100-5 disjoint 5-shot setting. Mean and standard deviation over 5 runs are reported.

Input: Figure 1: Our proposed method utilizes guided copy-paste augmentation to synthesize diverse training data, using as few as one single novel instance for training. For example, the model encounters an image of many motorcycles, which is novel to the model. As a result, the model incorrectly assigns learned bicycle labels to these pixels and therefore needs to be updated. Our proposed method can adapt to the novel motorcycle class with an annotation of a single motorcycle, which can be efficiently annotated; whereas previous work (Cermelli et al., 2020; 2021) require time-consuming annotation of all instances of motorcycles or even the entire image. Best view in color.

Input: Figure 7: Multi-step qualitative results of our method (GAPS) on the PASCAL-5-1 split under 1- shot setting. (Best view in color.)

Input: Figure 2: Overview of GAPS. During the incremental learning stage, GAPS takes in as few as one annotated instance of a single image. It is more probable for GAPS to select a scene contextually similar to the provided image from memory-replay buffer D̂0. The image is then probabilistically pasted to generate synthetic fully-labeled scenes. Note that GAPS is model-agnostic, and here we use PIFS (Cermelli et al., 2021) as an example for the underlying segmentation model to illustrate how GAPS is applied as a pre-processor. Best seen in color.

Input: Figure 5: Qualitative results on the PASCAL-5-1 split under 1-shot setting. Sequential adaptation to five classes are performed in the order indicated by the blue arrow on the left. Visualization is done after all adaptation steps are completed. Observe, particularly in rows 1, 3, and 4, that our method mitigates catastrophic forgetting and improves performance on both base and novel classes compared to PIFS and PIFS+NPS. (Best viewed in color)

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
Based on the evidence provided, I'll assess the claim: "More notably, GAPS maintains good performance in even more impoverished annotation settings, where only single instances of novel objects are annotated."

The evidence strongly supports this claim through:
- Quantitative results showing PIFS+GAPS maintains performance with single-instance annotations while other methods degrade significantly
- Visual examples demonstrating successful learning from single instances
- Thorough experimental validation across multiple datasets
- Recognition of limitations and balanced discussion of capabilities

The claim appears to be well-stated and accurately reflects the demonstrated capabilities without exaggeration. The evidence directly validates both the "maintains good performance" and "single instances" aspects.

<score>1</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

More notably, GAPS maintains good performance in even more impoverished annotation settings, where only single instances of novel objects are annotated.

The review comment to be evaluated is:

Strength:
 (1) This paper introduces copy-paste as a synthesis technique to few-shot incremental segmentation.
 (2) Experiments show clear improvement on performance with the proposed method.

Weaknesses:
(1)  My main concern is the novelty of the paper, copy-paste technique is more about a data augmentation methods, which has already been shown effective in many segmentation task, it is not novel enough to be accepted as an ICLR paper.
(2) The paper is not clear at least for me, especially figure 1 and figure, it's really hard for readers to understand the task, although the idea is clear.

The evidence to be evaluated is:However, existing work relies on rich annotations and tends to fail when only a limited amount of data is available. In contrast, our work enables incremental segmentation learning with _few data_ via a guided copy-paste process, which demonstrates promising performance under the few-shot and more impoverished single-instance setting.

During the _incremental learning stage_, a sequence of tasks \(\{D_{1},D_{2},\dots\}\) with novel categories is presented to the model, where \(D_{j}=\{(x_{i},y_{i})|x_{i}\in\mathcal{X},y_{i}\in\mathcal{Y}^{\mathcal{C}_{j}}\}\) and \(\mathcal{C}_{j}\) is a set of classes for task \(D_{j}\). In few-shot learning, the size of the training sets for the novel tasks is small, _i.e_., \(|D_{j}|\ll|\mathcal{D}_{0}|\).

To enforce the partially-annotated image setting, we follow Cermelli _et al_. and assume that only novel classes are annotated, _i.e_., \(\mathcal{C}_{i}\cap C_{j}=\varnothing\) for all \(i\neq j\).

It is a _generic and model-agnostic_ data synthesis framework that generates fully-labeled scenes from partially-annotated images of novel objects as a preprocessor to the underlying segmentation model.

During the incremental learning stage, fully-labeled samples are synthesized by copying from the masked novel objects in \(D_{1},\dots,D_{j}\) and pasting onto base exemplars from the replay buffer \(\hat{D}_{0}\).

In principle, GAPS is model-agnostic, which means that it can work with many incremental segmentation models as a diverse data source to improve their performance.

**GAPS consistently increases performance under few-shot settings.** Methods combined with our proposed data source, GAPS, consistently outperform their un-augmented counterpart on both the base and novel categories performance. It is worth noting that GAPS substantially boosts the performance of methods that originally require fully-annotated training images (i.e., fine-tuning and PIFS), despite using only partially-annotated images now. Even for methods that do not carry out end-to-end training and update only the classifier (i.e., SSUL), GAPS still steadily increases performance on novel categories.

**Frequency-guided probabilistic synthesis boosts results on novel classes**.

Though the semantic background shift proposed by Cermelli et al. (2020) relaxes the requirement to provide full segmentation annotations, it still requires _all novel instances_ in images to be annotated, which can be time-consuming to obtain in cluttered scenes and hinder potential applications. Here we consider a more challenging task setting, which we term _single-instance incremental learning_. Namely, for training images provided in incremental learning stages, if there are multiple instances of a novel class in the image, we assume that only _one instance_ will be annotated. To simulate this setting, we use the instance-level segmentation annotation provided by the COCO dataset to enforce only annotation of one novel instance in every image is available to the model.

We can observe that the pseudo-labeling baseline, \(\text{PIFS}(\mathcal{L}_{2})^{\dagger}\), yields substantially worse performance when the model receives single-instance annotations.

On the contrary, \(\text{PIFS}(\mathcal{L}_{2})\)+GAPS shows only a minor performance decrease with single instances. This highlights the potential of copy-paste synthesis as an alternative to the existing pseudo-labeling paradigm to cope with the more realistic single-instance setting.

In this paper, we demonstrate how judicious use of copy-paste dramatically boosts the performance of incremental segmentation methods under the few-shot setting and enables learning with partially-annotated images.

Finally, the ability to learn with as few as one annotated instance in an image raises several intriguing possibilities. For example, integrating our work with learning-based interactive segmentation will enable human operators to continually and adaptively teach novel classes and correct failed predictions. This workflow has many interesting applications such as robot teleoperation where sparse annotations are preferable. Learning with weaker annotations, like bounding boxes or single clicks, and even self-supervision, is also an interesting direction to explore.

In our implementation, the probability to synthesize novel objects is simply given by \(1/M\), where \(M\) is the sum of total number of base classes and seen novel classes.

In the result, we can observe that our method (vRFS) achieves the best result in terms of the novel IoU and harmonic mean of base and novel IoU.

We perform an additional experiment on COCO-20-1 where a long few-shot incremental learning sequence (60 base; 20 novel classes with 20 incremental steps; 5-shot) is used.

PIFS(\(\mathcal{L}_{2}\)), augmented with GAPS, demonstrates more robustness to forgetting with many incremental learning steps than standard PIFS. To validate our approach on the ADE20k dataset (Zhou et al., 2017, 2019), we ran a suite of experiments where we follow the ADE100-5 (11 steps) setup discussed in (Cha et al., 2021) and modify it as 5-shot learning. We tested PIFS(\(\mathcal{L}_{2}\)) and PIFS(\(\mathcal{L}_{2}\))+GAPS on this setting. The results are given in Table 9. We observe a steady increase in both the base IoU and novel IoU in this challenging task setting on methods augmented with GAPS.

Our PIFS(\(\mathcal{L}_{2}\))+GAPS significantly outperforms both baselines.

We conducted an additional experiment on the COCO-20-1 dataset under the 5-shot setting, where we vary the number of fully-annotated images in the memory-replay buffer to investigate the per formance of our method. As can be seen from Fig. 4, even with as few as 100 fully-annotated base images, GAPS still consistently boosts the performance of the underlying PIFS(\(\mathcal{L}_{2}\)) method.

Fig. 5 shows qualitative results of PIFS(\(\mathcal{L}_{2}\)), PIFS(\(\mathcal{L}_{2}\))+NPS (naive copy-paste), and PIFS(\(\mathcal{L}_{2}\))+GAPS on the PASCAL-5-1 split dataset under the 1-shot setting using images on the left column.

On the other hand, the model trained using GAPS not only yields good result on recently learned classes, but also maintains promising performance on classes adapted earlier.

On the other hand, PIFS(\(\mathcal{L}_{2}\))+GAPS is able to maintain good performance on learned classes as it adapts to new classes.

Input: Table 9: Performance of PIFS(L2) and PIFS(L2)+GAPS on the ADE100-5 disjoint 5-shot setting. Mean and standard deviation over 5 runs are reported.

Input: Figure 1: Our proposed method utilizes guided copy-paste augmentation to synthesize diverse training data, using as few as one single novel instance for training. For example, the model encounters an image of many motorcycles, which is novel to the model. As a result, the model incorrectly assigns learned bicycle labels to these pixels and therefore needs to be updated. Our proposed method can adapt to the novel motorcycle class with an annotation of a single motorcycle, which can be efficiently annotated; whereas previous work (Cermelli et al., 2020; 2021) require time-consuming annotation of all instances of motorcycles or even the entire image. Best view in color.

Input: Figure 7: Multi-step qualitative results of our method (GAPS) on the PASCAL-5-1 split under 1- shot setting. (Best view in color.)

Input: Figure 2: Overview of GAPS. During the incremental learning stage, GAPS takes in as few as one annotated instance of a single image. It is more probable for GAPS to select a scene contextually similar to the provided image from memory-replay buffer D̂0. The image is then probabilistically pasted to generate synthetic fully-labeled scenes. Note that GAPS is model-agnostic, and here we use PIFS (Cermelli et al., 2021) as an example for the underlying segmentation model to illustrate how GAPS is applied as a pre-processor. Best seen in color.

Input: Figure 5: Qualitative results on the PASCAL-5-1 split under 1-shot setting. Sequential adaptation to five classes are performed in the order indicated by the blue arrow on the left. Visualization is done after all adaptation steps are completed. Observe, particularly in rows 1, 3, and 4, that our method mitigates catastrophic forgetting and improves performance on both base and novel classes compared to PIFS and PIFS+NPS. (Best viewed in color)

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the evidence, the claim "GAPS maintains good performance in even more impoverished annotation settings, where only single instances of novel objects are annotated" appears to be well-stated and supported by the evidence. The paper demonstrates:

1. Quantitative results showing only minor performance decrease with single-instance annotations compared to significant drops in baseline methods
2. Detailed experimental validation across multiple datasets (PASCAL, COCO, ADE20k)
3. Qualitative results in figures showing effective learning from single instances
4. Clear acknowledgment of limitations and potential applications

The claim accurately reflects the evidence without exaggeration, and the review comments don't dispute this specific aspect of the work.

<score>1</score>