Summary of Vilco-bench: Video Language Continual Learning Benchmark, by Tianqi Tang et al.
ViLCo-Bench: VIdeo Language COntinual learning Benchmark
by Tianqi Tang, Shohreh Deldari, Hao Xue, Celso De Melo, Flora D. Salim
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel benchmark for continual learning in video language tasks, called ViLCo-Bench. This benchmark is designed to evaluate models that can adapt to new tasks while retaining prior knowledge. The dataset consists of 10-minute-long videos and corresponding language queries from publicly available datasets. To address the challenges of memory complexity, natural language complexity, and text-video misalignment, the paper presents a novel memory-efficient framework that incorporates self-supervised learning and mimics long-term and short-term memory effects. The proposed framework addresses the limitations of existing continual learning benchmarks by incorporating greater complexity in terms of video length, natural language queries, and text-video alignment. The ViLCo-Bench dataset is expected to serve as a critical tool for exploring the video-language domain, extending beyond conventional class-incremental tasks, and addressing complex and limited annotation issues. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to test how well AI models can learn from videos and text. This is important because it will help us make better AI models that can handle new tasks while still remembering what they learned before. The researchers created a special dataset called ViLCo-Bench that has 10-minute-long videos and corresponding language queries. They also developed a new way to train AI models using this data, which helps them learn from both the videos and text. This work is important because it will help us make better AI models that can understand and process complex information from videos and text. This is an exciting area of research that has many potential applications, such as making AI more useful for tasks like video summarization, question answering, and language translation. |
Keywords
» Artificial intelligence » Alignment » Continual learning » Question answering » Self supervised » Summarization » Translation
