README.md

---
license: cc-by-nc-sa-4.0
language:
- en
pretty_name: ICQ-Highlight
size_categories:
- 1K<n<10K
---
# Dataset Card for ICQ-Highlight

## Dataset Details
A dataset for Localizing Events in Videos with Multimodal Queries (Reference image + refinement text)

### Dataset Description

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Video understanding is a pivotal task in the digital era, yet the dynamic and multievent nature of videos makes them labor-intensive and computationally demanding to process. Thus, localizing a specific event given a semantic query has gained importance in both user-oriented applications like video search and academic research into video foundation models. A significant limitation in current research is that semantic queries are typically in natural language that depicts the semantics of the target event. This setting overlooks the potential for multimodal semantic queries composed of images and texts. To address this gap, we introduce a new benchmark, ICQ, for localizing events in videos with multimodal queries, along with a new evaluation dataset ICQ-Highlight. Our new benchmark aims to evaluate how well models can localize an event given a multimodal semantic query that consists of a reference image, which depicts the event, and a refinement text to adjust the images' semantics. To systematically benchmark model performance, we include 4 styles of reference images and 5 types of refinement texts, allowing us to explore model performance across different domains. We propose 3 adaptation methods that tailor existing models to our new setting and evaluate 10 SOTA models, ranging from specialized to large-scale foundation models. We believe this benchmark is an initial step toward investigating multimodal queries in video event localization. Our project can be found at httos://icq-benchmark.github.io/.


- **Curated by:** Gengyuan Zhang, Mang Ling Ada Fok
- **Funded by [optional]:**
  - Munich Center of Machine Learning
  - LMU Munich
- **Language(s) (NLP):** English
- **License:** Creative Commons Attribution Non Commercial Share Alike 4.0

### Dataset Sources [optional]

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- **Repository:** [ICQ-benchmark](https://github.com/icq-benchmark/icq-benchmark)
<!-- - **Paper [optional]:** [More Information Needed] -->
<!-- - **Demo [optional]:** [More Information Needed] -->

## Uses
The dataset shall only be used for research purposes.

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## Dataset Structure

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- icq_highlight_release.jsonl: annotation file
- val_style_*.zip: reference images

## Dataset Creation

### Curation Rationale

<!-- Motivation for the creation of this dataset. -->

We introduce our new evaluation dataset, ICQ-Highlight, as a testbed for Video Event Localization with Mulitmodal Queries. 
This dataset is built upon the validation set of QVHighlight, a popular natural-language query-based video localization dataset.
For each original query in QVHighlight, we construct multimodal semantic queries that incorporate reference images paired with refinement texts. 
Considering the reference image style distribution discussed earlier, \dataset features 4 variants based on different image styles. 

### Source Data

<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->

#### Data Collection and Processing

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We generate reference images based on the original natural language queries and refinement texts using a suite of state-of-the-art Text-to-Image models.
Image generation can suffer from significant imperfections in terms of semantic consistency and content safety. To address these issues, we implement a quality check in two stages: (1) We calculate the semantic similarity between the generated images and the text queries using BLIP2~\cite{li2023blip} encoders, eliminating samples that score lower than 0.2; (2) We perform human sanity check to replace images that are: i) semantically misaligned with the text, ii) mismatched with the required reference image style, iii) containing sensitive or unpleasant content (\eg, violent, racial, sexual content), counterintuitive elements, or obvious generation artifacts.


#### Who are the source data producers?
- We build on the annotation files provided by [QVHighlighs](https://github.com/jayleicn/moment_detr)
- We generate reference images based on the original natural language queries and refinement texts using a suite of state-of-the-art Text-to-Image models, including [DALL-E-2](https://openai.com/index/dall-e-2/) and [Stable Diffusion](\footnotehttps://stability.ai/stable-image).

<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->


### Annotations [optional]

<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->

#### Annotation process

<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->

We emphasize the meticulous crowd-sourced data curation and annotation effort applied to QVHighlight for 2 main reasons: 
(1) To introduce refinement texts, we purposefully modify the original semantics of text queries in QVHighlight to generate queries that are similar yet subtly different; 
(2) Given that the original queries in QVHighlight can be too simple and ambiguous to generate reasonable reference images, we add necessary annotations to ensure that the generated image queries are more relevant to the original video semantics. 

We employed human annotators to annotate and modify the natural language queries. Each query is annotated and reviewed by different annotators to ensure consistency. 

<!-- #### Who are the annotators? -->

<!-- This section describes the people or systems who created the annotations. -->


#### Personal and Sensitive Information

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->

Despite manual filtering, there might be unpleasant/hallucinating synthesized images. Please contact us if you find any image offensive.

## Bias, Risks, and Limitations
The dataset is synthesized and could include sensitive/unpleasant/hallucinating images.

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### Recommendations

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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

## Citation [optional]

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[TBD]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->

<!-- [More Information Needed] -->

- *cinematic*: cinematic style images
- *cartoon*: cartoon style images
- *scribble*: scribble style images
- *realistic*: realistic style images

<!-- ## More Information [optional]

[More Information Needed] -->

## Dataset Card Authors [optional]
- Gengyuan Zhang
- Mang Ling Ada Fok

## Dataset Card Contact
[email: Gengyuan Zhang]([email protected])