Video Flow as Time Series: Discovering Temporal Consistency and Variability for VideoQA

• URL: http://arxiv.org/abs/2504.05783v1
• Date: Tue, 08 Apr 2025 08:08:03 GMT
• Title: Video Flow as Time Series: Discovering Temporal Consistency and Variability for VideoQA
• Authors: Zijie Song, Zhenzhen Hu, Yixiao Ma, Jia Li, Richang Hong,
• Abstract summary: We introduce the Temporal Trio Transformer (T3T), a novel architecture that models time consistency and time variability.<n>The efficacy of the T3T is demonstrated through extensive testing on multiple VideoQA benchmark datasets.
• Score: 41.61905821058282
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video Question Answering (VideoQA) is a complex video-language task that demands a sophisticated understanding of both visual content and temporal dynamics. Traditional Transformer-style architectures, while effective in integrating multimodal data, often simplify temporal dynamics through positional encoding and fail to capture non-linear interactions within video sequences. In this paper, we introduce the Temporal Trio Transformer (T3T), a novel architecture that models time consistency and time variability. The T3T integrates three key components: Temporal Smoothing (TS), Temporal Difference (TD), and Temporal Fusion (TF). The TS module employs Brownian Bridge for capturing smooth, continuous temporal transitions, while the TD module identifies and encodes significant temporal variations and abrupt changes within the video content. Subsequently, the TF module synthesizes these temporal features with textual cues, facilitating a deeper contextual understanding and response accuracy. The efficacy of the T3T is demonstrated through extensive testing on multiple VideoQA benchmark datasets. Our results underscore the importance of a nuanced approach to temporal modeling in improving the accuracy and depth of video-based question answering.

Related papers

• Token-Efficient Long Video Understanding for Multimodal LLMs [101.70681093383365]
  STORM is a novel architecture incorporating a dedicated temporal encoder between the image encoder and the Video-LLMs.<n>We show that STORM achieves state-of-the-art results across various long video understanding benchmarks.
  arXiv  Detail & Related papers  (2025-03-06T06:17:38Z)
• Temporal Contrastive Learning for Video Temporal Reasoning in Large Vision-Language Models [44.99833362998488]
  Temporal Semantic Alignment via Dynamic Prompting (TSADP) is a novel framework that enhances temporal reasoning capabilities. We evaluate TSADP on the VidSitu dataset, augmented with enriched temporal annotations. Our analysis highlights the robustness, efficiency, and practical utility of TSADP, making it a step forward in the field of video-language understanding.
  arXiv  Detail & Related papers  (2024-12-16T02:37:58Z)
• MotionAura: Generating High-Quality and Motion Consistent Videos using Discrete Diffusion [3.7270979204213446]
  We present four key contributions to address the challenges of video processing.<n>First, we introduce the 3D Inverted Vector-Quantization Variencoenco Autocoder.<n>Second, we present MotionAura, a text-to-video generation framework.<n>Third, we propose a spectral transformer-based denoising network.<n>Fourth, we introduce a downstream task of Sketch Guided Videopainting.
  arXiv  Detail & Related papers  (2024-10-10T07:07:56Z)
• Temporal Reasoning Transfer from Text to Video [51.68487044397409]
  Video Large Language Models (Video LLMs) struggle with tracking temporal changes and reasoning about temporal relationships. We introduce the Textual Temporal reasoning Transfer (T3) to transfer temporal reasoning abilities from text to video domains. LongVA-7B model achieves competitive performance on comprehensive video benchmarks.
  arXiv  Detail & Related papers  (2024-10-08T16:10:29Z)
• FancyVideo: Towards Dynamic and Consistent Video Generation via Cross-frame Textual Guidance [47.88160253507823]
  We introduce FancyVideo, an innovative video generator that improves the existing text-control mechanism. CTGM incorporates the Temporal Information (TII), Temporal Affinity Refiner (TAR), and Temporal Feature Booster (TFB) at the beginning, middle, and end of cross-attention.
  arXiv  Detail & Related papers  (2024-08-15T14:47:44Z)
• SViTT: Temporal Learning of Sparse Video-Text Transformers [65.93031164906812]
  We propose SViTT, a sparse video-text architecture that performs multi-frame reasoning with significantly lower cost than naive transformers with dense attention. SViTT employs two forms of sparsity: edge sparsity that limits the query-key communications between tokens in self-attention, and sparsity that discards uninformative visual tokens.
  arXiv  Detail & Related papers  (2023-04-18T08:17:58Z)
• Temporally Consistent Transformers for Video Generation [80.45230642225913]
  To generate accurate videos, algorithms have to understand the spatial and temporal dependencies in the world. No established benchmarks on complex data exist for rigorously evaluating video generation with long temporal dependencies. We introduce the Temporally Consistent Transformer (TECO), a generative model that substantially improves long-term consistency while also reducing sampling time.
  arXiv  Detail & Related papers  (2022-10-05T17:15:10Z)
• Temporal Pyramid Transformer with Multimodal Interaction for Video Question Answering [13.805714443766236]
  Video question answering (VideoQA) is challenging given its multimodal combination of visual understanding and natural language understanding. This paper proposes a novel Temporal Pyramid Transformer (TPT) model with multimodal interaction for VideoQA.
  arXiv  Detail & Related papers  (2021-09-10T08:31:58Z)

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.