Year: 2024
Type: conference-paper
Abstract: The redundancy in videos and the quadratic scaling with input length of Transformer models lead to the need for sampling and selection from input videos.During the selection process, differentiable Top-K algorithms are employed to ensure an end-to-end training process.However, these methods not only restrict the level at which temporal information is captured but also introduce sorting noise and inaccuracies.In this paper, we revisit the keyframe selection strategy for VideoQA and propose a novel framework named Dual-Stream Keyframe Enhancement (DSKE) incorporating the enhancement of temporal granularity.To balance end-to-end sorting and hard ranking, we employ a dual-stream keyframe selection strategy by fusing the differentiable and non-differentiable results together to achieve a unified approach.One stream is based on the approximate ranking obtained from the differentiable Top-K algorithm, while the other stream utilizes the results obtained from hard ranking.We separately train decoders on the outputs of each stream and then combine the decoder results to predict the final answer.By integrating both stream results, DSKE effectively balances the inclusion of relevant information while filtering out noise.Additionally, we capture temporal variation information by incorporating a series of overlapping sliding time windows to enrich the temporal granularity.To evaluate the effectiveness of DSKE, we conduct experiments on the NExT-QA and AGQA benchmarks.The results demonstrate that our framework significantly improves the performance of VideoQA by effectively incorporating temporal components and enhancing the keyframe ranking process.
Authors: Zhenzhen Hu, Xin Guan, Jia Li, Zijie Song, Richang Hong
Institution: Hefei University of Technology
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4405868360