ReSpec: Relevance and Specificity Grounded Online Filtering for Learning on Video-Text Data Streams
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912338211241984 |
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| author | Kim, Chris Dongjoo Moon, Jihwan Moon, Sangwoo Yun, Heeseung Lee, Sihaeng Kembhavi, Aniruddha Lee, Soonyoung Kim, Gunhee Lee, Sangho Clark, Christopher |
| author_facet | Kim, Chris Dongjoo Moon, Jihwan Moon, Sangwoo Yun, Heeseung Lee, Sihaeng Kembhavi, Aniruddha Lee, Soonyoung Kim, Gunhee Lee, Sangho Clark, Christopher |
| contents | The rapid growth of video-text data presents challenges in storage and computation during training. Online learning, which processes streaming data in real-time, offers a promising solution to these issues while also allowing swift adaptations in scenarios demanding real-time responsiveness. One strategy to enhance the efficiency and effectiveness of learning involves identifying and prioritizing data that enhances performance on target downstream tasks. We propose Relevance and Specificity-based online filtering framework (ReSpec) that selects data based on four criteria: (i) modality alignment for clean data, (ii) task relevance for target focused data, (iii) specificity for informative and detailed data, and (iv) efficiency for low-latency processing. Relevance is determined by the probabilistic alignment of incoming data with downstream tasks, while specificity employs the distance to a root embedding representing the least specific data as an efficient proxy for informativeness. By establishing reference points from target task data, ReSpec filters incoming data in real-time, eliminating the need for extensive storage and compute. Evaluating on large-scale datasets WebVid2M and VideoCC3M, ReSpec attains state-of-the-art performance on five zeroshot video retrieval tasks, using as little as 5% of the data while incurring minimal compute. The source code is available at https://github.com/cdjkim/ReSpec. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_14875 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ReSpec: Relevance and Specificity Grounded Online Filtering for Learning on Video-Text Data Streams Kim, Chris Dongjoo Moon, Jihwan Moon, Sangwoo Yun, Heeseung Lee, Sihaeng Kembhavi, Aniruddha Lee, Soonyoung Kim, Gunhee Lee, Sangho Clark, Christopher Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning The rapid growth of video-text data presents challenges in storage and computation during training. Online learning, which processes streaming data in real-time, offers a promising solution to these issues while also allowing swift adaptations in scenarios demanding real-time responsiveness. One strategy to enhance the efficiency and effectiveness of learning involves identifying and prioritizing data that enhances performance on target downstream tasks. We propose Relevance and Specificity-based online filtering framework (ReSpec) that selects data based on four criteria: (i) modality alignment for clean data, (ii) task relevance for target focused data, (iii) specificity for informative and detailed data, and (iv) efficiency for low-latency processing. Relevance is determined by the probabilistic alignment of incoming data with downstream tasks, while specificity employs the distance to a root embedding representing the least specific data as an efficient proxy for informativeness. By establishing reference points from target task data, ReSpec filters incoming data in real-time, eliminating the need for extensive storage and compute. Evaluating on large-scale datasets WebVid2M and VideoCC3M, ReSpec attains state-of-the-art performance on five zeroshot video retrieval tasks, using as little as 5% of the data while incurring minimal compute. The source code is available at https://github.com/cdjkim/ReSpec. |
| title | ReSpec: Relevance and Specificity Grounded Online Filtering for Learning on Video-Text Data Streams |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2504.14875 |