Enhancing Composed Video Retrieval with Detailed Descriptions

Core Concepts

Detailed language descriptions enhance Composed Video Retrieval by providing contextual information for better alignment.

Abstract

The content introduces a novel Composed Video Retrieval (CoVR) framework that leverages detailed language descriptions to encode query-specific contextual information. It addresses the challenges of bridging the domain gap and aligning multi-modal feature embeddings for accurate video retrieval. The proposed approach shows significant performance gains in both CoVR and zero-shot CoIR tasks. Introduction CoIR vs. CoVR challenges. Importance of detailed language descriptions. Related Work Progress in image and video retrieval. Evolution from content-based to multi-modal approaches. Method Problem statement for CoVR. Baseline framework overview. Proposed architecture design focusing on query-specific context preservation and discriminative embeddings. Experiments Evaluation on WebVid-CoVR dataset. Ablation study on inputs, target datasets, and training losses. Impact of detailed descriptions on performance improvement. Results Superior performance compared to baseline CoVR-BLIP [43]. Notable gains in recall rates at different K values. Conclusion Detailed descriptions play a crucial role in enhancing Composed Video Retrieval performance.

Stats

"Experiments on three datasets show that our approach obtains state-of-the-art performance." "Our approach achieves a gain of ≈7% in terms of recall@K=1 score." "Our approach leads to superior performance compared to the original [43]."

Quotes

"Detailed language descriptions provide complementary contextual information." "Our joint multi-modal embeddings leveraging language information are closer to target embeddings."

Key Insights Distilled From

Composed Video Retrieval via Enriched Context and Discriminative Embeddings

by Omkar Thawak... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16997.pdf

Deeper Inquiries

How can detailed language descriptions be further optimized for improved video retrieval?

Detailed language descriptions can be further optimized for improved video retrieval by considering the following strategies: Semantic Understanding: Enhance the language model to have a deeper semantic understanding of the content in videos. This can involve training the model on a larger and more diverse dataset to capture a wider range of contexts and nuances. Contextual Relevance: Ensure that the generated descriptions are contextually relevant to the visual content in the videos. This can be achieved by incorporating contextual information from surrounding frames or scenes. Quality Control: Implement quality control measures to filter out irrelevant or inaccurate descriptions. This could involve human validation or feedback loops to continuously improve the accuracy of generated descriptions. Multimodal Fusion: Explore techniques for better fusion of textual and visual information, such as attention mechanisms or cross-modal embeddings, to create a more cohesive representation of videos based on both modalities. Fine-tuning Strategies: Experiment with fine-tuning approaches that adapt pre-trained models specifically for video retrieval tasks, optimizing them for capturing key details and relationships between text and visuals.

How might this framework impact other areas beyond video retrieval, such as content recommendation systems?

This framework has implications beyond video retrieval and can potentially enhance various other areas like content recommendation systems: Image Retrieval: The same principles applied in composed video retrieval can be adapted for image-based search engines, enabling users to find visually similar images based on detailed textual queries. E-commerce: In e-commerce platforms, this framework could improve product recommendations by allowing users to search using detailed natural language descriptions along with images, leading to more accurate matches with their preferences. Personalized Advertising: By leveraging detailed language descriptions alongside visual cues, advertisers could create more targeted ad campaigns tailored towards individual preferences and interests. Healthcare Imaging: Medical professionals could benefit from enhanced image search capabilities where they describe specific medical conditions or symptoms in detail to retrieve relevant diagnostic images quickly and accurately.

What are the potential limitations or biases introduced by relying heavily on text-based modifications?

Relying heavily on text-based modifications in video retrieval may introduce several limitations and biases: Language Ambiguity: Textual descriptions may not always convey precise meanings due to linguistic ambiguity, leading to misinterpretations when matching them with visual content. 2 .Cultural Biases: Language is inherently influenced by cultural norms and perspectives which may result in biased interpretations when used as modification texts. 3 .Limited Vocabulary: The effectiveness of text-based modifications is constrained by vocabulary limitations which might hinder accurate representations of complex concepts. 4 .Overfitting Risk: Depending too much on specific textual modifications may lead models towards overfitting certain patterns present only in training data but not generalizable across different datasets. 5 .Subjectivity Bias: Textual inputs are subjective; different individuals may describe visuals differently based on personal experiences or perceptions introducing bias into the system. These limitations highlight the importance of balancing text-based modifications with other modalities while also implementing robust validation processes during model development and deployment phases."