STOP: Integrated Spatial-Temporal Dynamic Prompting for Video Understanding

• URL: http://arxiv.org/abs/2503.15973v2
• Date: Tue, 25 Mar 2025 03:05:36 GMT
• Title: STOP: Integrated Spatial-Temporal Dynamic Prompting for Video Understanding
• Authors: Zichen Liu, Kunlun Xu, Bing Su, Xu Zou, Yuxin Peng, Jiahuan Zhou,
• Abstract summary: We propose an integrated Spatial-TempOral dynamic Prompting (STOP) model.<n>It consists of two complementary modules, the intra-frame spatial prompting and inter-frame temporal prompting.<n>STOP consistently achieves superior performance against state-of-the-art methods.
• Score: 48.12128042470839
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pre-trained on tremendous image-text pairs, vision-language models like CLIP have demonstrated promising zero-shot generalization across numerous image-based tasks. However, extending these capabilities to video tasks remains challenging due to limited labeled video data and high training costs. Recent video prompting methods attempt to adapt CLIP for video tasks by introducing learnable prompts, but they typically rely on a single static prompt for all video sequences, overlooking the diverse temporal dynamics and spatial variations that exist across frames. This limitation significantly hinders the model's ability to capture essential temporal information for effective video understanding. To address this, we propose an integrated Spatial-TempOral dynamic Prompting (STOP) model which consists of two complementary modules, the intra-frame spatial prompting and inter-frame temporal prompting. Our intra-frame spatial prompts are designed to adaptively highlight discriminative regions within each frame by leveraging intra-frame attention and temporal variation, allowing the model to focus on areas with substantial temporal dynamics and capture fine-grained spatial details. Additionally, to highlight the varying importance of frames for video understanding, we further introduce inter-frame temporal prompts, dynamically inserting prompts between frames with high temporal variance as measured by frame similarity. This enables the model to prioritize key frames and enhances its capacity to understand temporal dependencies across sequences. Extensive experiments on various video benchmarks demonstrate that STOP consistently achieves superior performance against state-of-the-art methods. The code is available at https://github.com/zhoujiahuan1991/CVPR2025-STOP.

Related papers

• Tracktention: Leveraging Point Tracking to Attend Videos Faster and Better [61.381599921020175]
  Temporal consistency is critical in video prediction to ensure that outputs are coherent and free of artifacts. Traditional methods, such as temporal attention and 3D convolution, may struggle with significant object motion. We propose the Tracktention Layer, a novel architectural component that explicitly integrates motion information using point tracks.
  arXiv  Detail & Related papers  (2025-03-25T17:58:48Z)
• 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.<n>We evaluate TSADP on the VidSitu dataset, augmented with enriched temporal annotations.<n>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)
• 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)
• Structured Video-Language Modeling with Temporal Grouping and Spatial Grounding [112.3913646778859]
  We propose a simple yet effective video-language modeling framework, S-ViLM. It includes two novel designs, inter-clip spatial grounding and intra-clip temporal grouping, to promote learning region-object alignment and temporal-aware features. S-ViLM surpasses the state-of-the-art methods substantially on four representative downstream tasks.
  arXiv  Detail & Related papers  (2023-03-28T22:45:07Z)
• Alignment-guided Temporal Attention for Video Action Recognition [18.5171795689609]
  We show that frame-by-frame alignments have the potential to increase the mutual information between frame representations. We propose Alignment-guided Temporal Attention (ATA) to extend 1-dimensional temporal attention with parameter-free patch-level alignments between neighboring frames.
  arXiv  Detail & Related papers  (2022-09-30T23:10:47Z)
• Leveraging Local Temporal Information for Multimodal Scene Classification [9.548744259567837]
  Video scene classification models should capture the spatial (pixel-wise) and temporal (frame-wise) characteristics of a video effectively. Transformer models with self-attention which are designed to get contextualized representations for individual tokens given a sequence of tokens, are becoming increasingly popular in many computer vision tasks. We propose a novel self-attention block that leverages both local and global temporal relationships between the video frames to obtain better contextualized representations for the individual frames.
  arXiv  Detail & Related papers  (2021-10-26T19:58:32Z)
• StyleVideoGAN: A Temporal Generative Model using a Pretrained StyleGAN [70.31913835035206]
  We present a novel approach to the video synthesis problem that helps to greatly improve visual quality. We make use of a pre-trained StyleGAN network, the latent space of which allows control over the appearance of the objects it was trained for. Our temporal architecture is then trained not on sequences of RGB frames, but on sequences of StyleGAN latent codes.
  arXiv  Detail & Related papers  (2021-07-15T09:58:15Z)
• Learning by Aligning Videos in Time [10.075645944474287]
  We present a self-supervised approach for learning video representations using temporal video alignment as a pretext task. We leverage a novel combination of temporal alignment loss and temporal regularization terms, which can be used as supervision signals for training an encoder network.
  arXiv  Detail & Related papers  (2021-03-31T17:55:52Z)
• BiST: Bi-directional Spatio-Temporal Reasoning for Video-Grounded Dialogues [95.8297116307127]
  We propose Bi-directional Spatio-Temporal Learning (BiST), a vision-language neural framework for high-resolution queries in videos. Specifically, our approach exploits both spatial and temporal-level information, and learns dynamic information diffusion between the two feature spaces. BiST achieves competitive performance and generates reasonable responses on a large-scale AVSD benchmark.
  arXiv  Detail & Related papers  (2020-10-20T07:43:00Z)

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.