End-to-End Video Question Answering with Frame Scoring Mechanisms and Adaptive Sampling
- URL: http://arxiv.org/abs/2407.15047v2
- Date: Tue, 23 Jul 2024 14:56:22 GMT
- Title: End-to-End Video Question Answering with Frame Scoring Mechanisms and Adaptive Sampling
- Authors: Jianxin Liang, Xiaojun Meng, Yueqian Wang, Chang Liu, Qun Liu, Dongyan Zhao,
- Abstract summary: We propose VidF4, a novel VideoQA framework equipped with tailored frame selection strategy for effective and efficient VideoQA.
We propose three frame-scoring mechanisms that consider both question relevance and inter-frame similarity to evaluate the importance of each frame for a given question on the video.
The experimental results across three widely adopted benchmarks demonstrate that our model consistently outperforms existing VideoQA methods.
- Score: 43.024232182899354
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Video Question Answering (VideoQA) has emerged as a challenging frontier in the field of multimedia processing, requiring intricate interactions between visual and textual modalities. Simply uniformly sampling frames or indiscriminately aggregating frame-level visual features often falls short in capturing the nuanced and relevant contexts of videos to well perform VideoQA. To mitigate these issues, we propose VidF4, a novel VideoQA framework equipped with tailored frame selection strategy for effective and efficient VideoQA. We propose three frame-scoring mechanisms that consider both question relevance and inter-frame similarity to evaluate the importance of each frame for a given question on the video. Furthermore, we design a differentiable adaptive frame sampling mechanism to facilitate end-to-end training for the frame selector and answer generator. The experimental results across three widely adopted benchmarks demonstrate that our model consistently outperforms existing VideoQA methods, establishing a new SOTA across NExT-QA (+0.3%), STAR (+0.9%), and TVQA (+1.0%). Furthermore, through both quantitative and qualitative analyses, we validate the effectiveness of each design choice.
Related papers
- An Empirical Comparison of Video Frame Sampling Methods for Multi-Modal RAG Retrieval [1.6581184950812533]
We investigate the trade-offs in frame sampling methods for Video & Frame Retrieval using natural language questions.
Our study focuses on the storage and retrieval of image data (video frames) within a vector database required by Video RAG pattern.
arXiv Detail & Related papers (2024-07-22T11:44:08Z) - CLIPVQA:Video Quality Assessment via CLIP [56.94085651315878]
We propose an efficient CLIP-based Transformer method for the VQA problem ( CLIPVQA)
The proposed CLIPVQA achieves new state-of-the-art VQA performance and up to 37% better generalizability than existing benchmark VQA methods.
arXiv Detail & Related papers (2024-07-06T02:32:28Z) - An Empirical Study of Frame Selection for Text-to-Video Retrieval [62.28080029331507]
Text-to-video retrieval (TVR) aims to find the most relevant video in a large video gallery given a query text.
Existing methods typically select a subset of frames within a video to represent the video content for TVR.
In this paper, we make the first empirical study of frame selection for TVR.
arXiv Detail & Related papers (2023-11-01T05:03:48Z) - Search-Map-Search: A Frame Selection Paradigm for Action Recognition [21.395733318164393]
Frame selection aims to extract the most informative and representative frames to help a model better understand video content.
Existing frame selection methods either individually sample frames based on per-frame importance prediction, or adopt reinforcement learning agents to find representative frames in succession.
We propose a Search-Map-Search learning paradigm which combines the advantages of search and supervised learning to select the best combination of frames from a video as one entity.
arXiv Detail & Related papers (2023-04-20T13:49:53Z) - PeQuENet: Perceptual Quality Enhancement of Compressed Video with
Adaptation- and Attention-based Network [27.375830262287163]
We propose a generative adversarial network (GAN) framework to enhance the perceptual quality of compressed videos.
Our framework includes attention and adaptation to different quantization parameters (QPs) in a single model.
Experimental results demonstrate the superior performance of the proposed PeQuENet compared with the state-of-the-art compressed video quality enhancement algorithms.
arXiv Detail & Related papers (2022-06-16T02:49:28Z) - OCSampler: Compressing Videos to One Clip with Single-step Sampling [82.0417131211353]
We propose a framework named OCSampler to explore a compact yet effective video representation with one short clip.
Our basic motivation is that the efficient video recognition task lies in processing a whole sequence at once rather than picking up frames sequentially.
arXiv Detail & Related papers (2022-01-12T09:50:38Z) - Condensing a Sequence to One Informative Frame for Video Recognition [113.3056598548736]
This paper studies a two-step alternative that first condenses the video sequence to an informative "frame"
A valid question is how to define "useful information" and then distill from a sequence down to one synthetic frame.
IFS consistently demonstrates evident improvements on image-based 2D networks and clip-based 3D networks.
arXiv Detail & Related papers (2022-01-11T16:13:43Z) - DeepQAMVS: Query-Aware Hierarchical Pointer Networks for Multi-Video
Summarization [127.16984421969529]
We introduce a novel Query-Aware Hierarchical Pointer Network for Multi-Video Summarization, termed DeepQAMVS.
DeepQAMVS is trained with reinforcement learning, incorporating rewards that capture representativeness, diversity, query-adaptability and temporal coherence.
We achieve state-of-the-art results on the MVS1K dataset, with inference time scaling linearly with the number of input video frames.
arXiv Detail & Related papers (2021-05-13T17:33:26Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.