Shifting Perspective to See Difference: A Novel Multi-View Method for Skeleton based Action Recognition

• URL: http://arxiv.org/abs/2209.02986v1
• Date: Wed, 7 Sep 2022 08:20:37 GMT
• Title: Shifting Perspective to See Difference: A Novel Multi-View Method for Skeleton based Action Recognition
• Authors: Ruijie Hou, Yanran Li, Ningyu Zhang, Yulin Zhou, Xiaosong Yang, Zhao Wang
• Abstract summary: Skeleton-based human action recognition is a longstanding challenge due to its complex dynamics. We propose a conceptually simple yet effective Multi-view strategy that recognizes actions from a collection of dynamic view features. Our module can work seamlessly with the existing action classification model.
• Score: 22.004971546763162
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Skeleton-based human action recognition is a longstanding challenge due to its complex dynamics. Some fine-grain details of the dynamics play a vital role in classification. The existing work largely focuses on designing incremental neural networks with more complicated adjacent matrices to capture the details of joints relationships. However, they still have difficulties distinguishing actions that have broadly similar motion patterns but belong to different categories. Interestingly, we found that the subtle differences in motion patterns can be significantly amplified and become easy for audience to distinct through specified view directions, where this property haven't been fully explored before. Drastically different from previous work, we boost the performance by proposing a conceptually simple yet effective Multi-view strategy that recognizes actions from a collection of dynamic view features. Specifically, we design a novel Skeleton-Anchor Proposal (SAP) module which contains a Multi-head structure to learn a set of views. For feature learning of different views, we introduce a novel Angle Representation to transform the actions under different views and feed the transformations into the baseline model. Our module can work seamlessly with the existing action classification model. Incorporated with baseline models, our SAP module exhibits clear performance gains on many challenging benchmarks. Moreover, comprehensive experiments show that our model consistently beats down the state-of-the-art and remains effective and robust especially when dealing with corrupted data. Related code will be available on https://github.com/ideal-idea/SAP .

Related papers

• XR-VLM: Cross-Relationship Modeling with Multi-part Prompts and Visual Features for Fine-Grained Recognition [20.989787824067143]
  XR-VLM is a novel mechanism to discover subtle differences by modeling cross-relationships. We develop a multi-part prompt learning module to capture multi-perspective descriptions. Our method achieves significant improvements compared to current state-of-the-art approaches.
  arXiv  Detail & Related papers  (2025-03-10T08:58:05Z)
• Analyzing Fine-tuning Representation Shift for Multimodal LLMs Steering alignment [53.90425382758605]
  We show how fine-tuning alters the internal structure of a model to specialize in new multimodal tasks. Our work sheds light on how multimodal representations evolve through fine-tuning and offers a new perspective for interpreting model adaptation in multimodal tasks.
  arXiv  Detail & Related papers  (2025-01-06T13:37:13Z)
• Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models [50.98559225639266]
  We investigate the contributions of visual features from different encoder layers using 18 benchmarks spanning 6 task categories. Our findings reveal that multilayer features provide complementary strengths with varying task dependencies, and uniform fusion leads to suboptimal performance. We propose the instruction-guided vision aggregator, a module that dynamically integrates multi-layer visual features based on textual instructions.
  arXiv  Detail & Related papers  (2024-12-26T05:41:31Z)
• An Information Compensation Framework for Zero-Shot Skeleton-based Action Recognition [49.45660055499103]
  Zero-shot human skeleton-based action recognition aims to construct a model that can recognize actions outside the categories seen during training. Previous research has focused on aligning sequences' visual and semantic spatial distributions. We introduce a new loss function sampling method to obtain a tight and robust representation.
  arXiv  Detail & Related papers  (2024-06-02T06:53:01Z)
• RefSAM: Efficiently Adapting Segmenting Anything Model for Referring Video Object Segmentation [53.4319652364256]
  This paper presents the RefSAM model, which explores the potential of SAM for referring video object segmentation. Our proposed approach adapts the original SAM model to enhance cross-modality learning by employing a lightweight Cross-RValModal. We employ a parameter-efficient tuning strategy to align and fuse the language and vision features effectively.
  arXiv  Detail & Related papers  (2023-07-03T13:21:58Z)
• An Efficient General-Purpose Modular Vision Model via Multi-Task Heterogeneous Training [79.78201886156513]
  We present a model that can perform multiple vision tasks and can be adapted to other downstream tasks efficiently. Our approach achieves comparable results to single-task state-of-the-art models and demonstrates strong generalization on downstream tasks.
  arXiv  Detail & Related papers  (2023-06-29T17:59:57Z)
• Multi-Modal Few-Shot Temporal Action Detection [157.96194484236483]
  Few-shot (FS) and zero-shot (ZS) learning are two different approaches for scaling temporal action detection to new classes. We introduce a new multi-modality few-shot (MMFS) TAD problem, which can be considered as a marriage of FS-TAD and ZS-TAD.
  arXiv  Detail & Related papers  (2022-11-27T18:13:05Z)
• Modular Networks Prevent Catastrophic Interference in Model-Based Multi-Task Reinforcement Learning [0.8883733362171032]
  We study whether model-based multi-task reinforcement learning benefits from shared dynamics models in a similar way model-free methods do from shared policy networks. Using a single dynamics model, we see clear evidence of task confusion and reduced performance. As a remedy, enforcing an internal structure for the learned dynamics model by training isolated sub-networks for each task notably improves performance.
  arXiv  Detail & Related papers  (2021-11-15T12:31:31Z)
• Visual Concept Reasoning Networks [93.99840807973546]
  A split-transform-merge strategy has been broadly used as an architectural constraint in convolutional neural networks for visual recognition tasks. We propose to exploit this strategy and combine it with our Visual Concept Reasoning Networks (VCRNet) to enable reasoning between high-level visual concepts. Our proposed model, VCRNet, consistently improves the performance by increasing the number of parameters by less than 1%.
  arXiv  Detail & Related papers  (2020-08-26T20:02:40Z)
• Bowtie Networks: Generative Modeling for Joint Few-Shot Recognition and Novel-View Synthesis [39.53519330457627]
  We propose a novel task of joint few-shot recognition and novel-view synthesis. We aim to simultaneously learn an object classifier and generate images of that type of object from new viewpoints. We focus on the interaction and cooperation between a generative model and a discriminative model.
  arXiv  Detail & Related papers  (2020-08-16T19:40:56Z)
• Dynamic Feature Integration for Simultaneous Detection of Salient Object, Edge and Skeleton [108.01007935498104]
  In this paper, we solve three low-level pixel-wise vision problems, including salient object segmentation, edge detection, and skeleton extraction. We first show some similarities shared by these tasks and then demonstrate how they can be leveraged for developing a unified framework.
  arXiv  Detail & Related papers  (2020-04-18T11:10:11Z)

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.