State Space Prompting via Gathering and Spreading Spatio-Temporal Information for Video Understanding

• URL: http://arxiv.org/abs/2510.12160v1
• Date: Tue, 14 Oct 2025 05:30:36 GMT
• Title: State Space Prompting via Gathering and Spreading Spatio-Temporal Information for Video Understanding
• Authors: Jiahuan Zhou, Kai Zhu, Zhenyu Cui, Zichen Liu, Xu Zou, Gang Hua,
• Abstract summary: We propose a State Space Prompting (SSP) method for video understanding.<n>SSP combines intra-frame inter-frame prompts to aggregate and propagate keytemporal information in the video.<n>Our SSP significantly outperforms existing SOTA methods by 2.76% on average.
• Score: 50.866929044215965
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, pre-trained state space models have shown great potential for video classification, which sequentially compresses visual tokens in videos with linear complexity, thereby improving the processing efficiency of video data while maintaining high performance. To apply powerful pre-trained models to downstream tasks, prompt learning is proposed to achieve efficient downstream task adaptation with only a small number of fine-tuned parameters. However, the sequentially compressed visual prompt tokens fail to capture the spatial and temporal contextual information in the video, thus limiting the effective propagation of spatial information within a video frame and temporal information between frames in the state compression model and the extraction of discriminative information. To tackle the above issue, we proposed a State Space Prompting (SSP) method for video understanding, which combines intra-frame and inter-frame prompts to aggregate and propagate key spatiotemporal information in the video. Specifically, an Intra-Frame Gathering (IFG) module is designed to aggregate spatial key information within each frame. Besides, an Inter-Frame Spreading (IFS) module is designed to spread discriminative spatio-temporal information across different frames. By adaptively balancing and compressing key spatio-temporal information within and between frames, our SSP effectively propagates discriminative information in videos in a complementary manner. Extensive experiments on four video benchmark datasets verify that our SSP significantly outperforms existing SOTA methods by 2.76% on average while reducing the overhead of fine-tuning parameters.

Related papers

• KFFocus: Highlighting Keyframes for Enhanced Video Understanding [33.69757683688046]
  We propose KFFocus, a method designed to efficiently compress video tokens and emphasize the informative context present within video frames.<n>By assigning varying condensation ratios to frames based on their contextual relevance, KFFocus efficiently reduces token redundancy while preserving informative content details.<n>We also introduce a multimodal modeling module that encodes both the temporal relationships between video frames and the spatial structure within each frame.
  arXiv  Detail & Related papers  (2025-08-12T14:57:03Z)
• Exploiting Temporal State Space Sharing for Video Semantic Segmentation [53.8810901249897]
  Video semantic segmentation (VSS) plays a vital role in understanding the temporal evolution of scenes.<n>Traditional methods often segment videos frame-by-frame or in a short temporal window, leading to limited temporal context, redundant computations, and heavy memory requirements.<n>We introduce a Temporal Video State Space Sharing architecture to leverage Mamba state space models for temporal feature sharing.<n>Our model features a selective gating mechanism that efficiently propagates relevant information across video frames, eliminating the need for a memory-heavy feature pool.
  arXiv  Detail & Related papers  (2025-03-26T01:47:42Z)
• STOP: Integrated Spatial-Temporal Dynamic Prompting for Video Understanding [48.12128042470839]
  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.
  arXiv  Detail & Related papers  (2025-03-20T09:16:20Z)
• Spatiotemporal Attention-based Semantic Compression for Real-time Video Recognition [117.98023585449808]
  We propose a temporal attention-based autoencoder (STAE) architecture to evaluate the importance of frames and pixels in each frame. We develop a lightweight decoder that leverages a 3D-2D CNN combined to reconstruct missing information. Experimental results show that ViT_STAE can compress the video dataset H51 by 104x with only 5% accuracy loss.
  arXiv  Detail & Related papers  (2023-05-22T07:47:27Z)
• You Can Ground Earlier than See: An Effective and Efficient Pipeline for Temporal Sentence Grounding in Compressed Videos [56.676761067861236]
  Given an untrimmed video, temporal sentence grounding aims to locate a target moment semantically according to a sentence query. Previous respectable works have made decent success, but they only focus on high-level visual features extracted from decoded frames. We propose a new setting, compressed-domain TSG, which directly utilizes compressed videos rather than fully-decompressed frames as the visual input.
  arXiv  Detail & Related papers  (2023-03-14T12:53:27Z)
• Continuous Space-Time Video Super-Resolution Utilizing Long-Range Temporal Information [48.20843501171717]
  We propose a continuous ST-VSR (CSTVSR) method that can convert the given video to any frame rate and spatial resolution. We show that the proposed algorithm has good flexibility and achieves better performance on various datasets.
  arXiv  Detail & Related papers  (2023-02-26T08:02:39Z)
• Video Captioning in Compressed Video [1.953018353016675]
  We propose a video captioning method which operates directly on the stored compressed videos. To learn a discriminative visual representation for video captioning, we design a residuals-assisted encoder (RAE), which spots regions of interest in I-frames. We evaluate our method on two benchmark datasets and demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2021-01-02T03:06:03Z)

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.