are listed as follows. Strengths:
The paper is easy to read, and the proposed idea is also easy to follow. Figure 1 can help the understanding of the proposed model.
The proposed model does not need the manual labeled relationship between semantic knowledge and target categories, and this may further reduce the supervision for knowledge graph construction. Weaknesses:
Incomplete comparison with existing works: the authors should list a more complete comparison table to compare the results of existing works. Some existing works can perform competitive or even superior results compared with the proposed model under the same feature backbone (ResNet-12).
mini-ImageNet tiered-ImageNet
1-shot 5-shot 1-shot 5-shot
Proposed 63.29+-0.71%. 80.12+-0.22% 66.69+-0.75%. 83.04+-0.61%
CAN[1] 63.85+-0.48%. 79.44+-0.34% 69.89+-0.51%. 84.23+-0.37%
CAN +T[1] 67.19+-0.55%. 80.64+-0.35% 73.21+-0.58%. 84.93+-0.38%
Comparison with unitary modality methods is misleading, as these baseline methods do not use any external information for the training and inference. The proposed method should consider other cross-modal few-shot classification works as baseline methods.
As the proposed model contains several modules, such as knowledge graph construction, graph neural network, and other few linear layers between components, this makes the reader hard to understand which module is crucial. It will be great if the authors could provide an ablation study to verify the effectiveness of each module in the next version of this paper. For example, the authors could provide studies like 1) what is the performance if the semantic information is removed in the knowledge graph, or 2) how the number of graph neural layers affects the overall performance.
Overall, the paper is easy to read. The idea of integrating semantic information in few-shot classification is interesting while it has been widely explored in existing works. Given the reported results of the proposed model and lack of analyses in the proposed model, I am inclined to the score "Ok but not good enough - rejection".
[1] “Cross Attention Network for Few-shot Classification”, Hou et al., NeurIPS ‘19
2) how the number of graph neural layers affects the overall performance. Overall, the paper is easy to read. The idea of integrating semantic information in few-shot classification is interesting while it has been widely explored in existing works. Given the reported results of the proposed model and lack of analyses in the proposed model, I am inclined to the score "Ok but not good enough - rejection". [1] “Cross Attention Network for Few-shot Classification”, Hou et al., NeurIPS ‘19