Uncertainty-Guided Appearance-Motion Association Network for Out-of-Distribution Action Detection

• URL: http://arxiv.org/abs/2409.09953v1
• Date: Mon, 16 Sep 2024 02:53:49 GMT
• Title: Uncertainty-Guided Appearance-Motion Association Network for Out-of-Distribution Action Detection
• Authors: Xiang Fang, Arvind Easwaran, Blaise Genest,
• Abstract summary: Out-of-distribution (OOD) detection targets to detect and reject test samples with semantic shifts. We propose a novel Uncertainty-Guided Appearance-Motion Association Network (UAAN) We show that UAAN beats state-of-the-art methods by a significant margin, illustrating its effectiveness.
• Score: 4.938957922033169
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Out-of-distribution (OOD) detection targets to detect and reject test samples with semantic shifts, to prevent models trained on in-distribution (ID) dataset from producing unreliable predictions. Existing works only extract the appearance features on image datasets, and cannot handle dynamic multimedia scenarios with much motion information. Therefore, we target a more realistic and challenging OOD detection task: OOD action detection (ODAD). Given an untrimmed video, ODAD first classifies the ID actions and recognizes the OOD actions, and then localizes ID and OOD actions. To this end, in this paper, we propose a novel Uncertainty-Guided Appearance-Motion Association Network (UAAN), which explores both appearance features and motion contexts to reason spatial-temporal inter-object interaction for ODAD.Firstly, we design separate appearance and motion branches to extract corresponding appearance-oriented and motion-aspect object representations. In each branch, we construct a spatial-temporal graph to reason appearance-guided and motion-driven inter-object interaction. Then, we design an appearance-motion attention module to fuse the appearance and motion features for final action detection. Experimental results on two challenging datasets show that UAAN beats state-of-the-art methods by a significant margin, illustrating its effectiveness.

Related papers

• Simultaneous Detection and Interaction Reasoning for Object-Centric Action Recognition [21.655278000690686]
  We propose an end-to-end object-centric action recognition framework. It simultaneously performs Detection And Interaction Reasoning in one stage. We conduct experiments on two datasets, Something-Else and Ikea-Assembly.
  arXiv  Detail & Related papers  (2024-04-18T05:06:12Z)
• Disentangled Interaction Representation for One-Stage Human-Object Interaction Detection [70.96299509159981]
  Human-Object Interaction (HOI) detection is a core task for human-centric image understanding. Recent one-stage methods adopt a transformer decoder to collect image-wide cues that are useful for interaction prediction. Traditional two-stage methods benefit significantly from their ability to compose interaction features in a disentangled and explainable manner.
  arXiv  Detail & Related papers  (2023-12-04T08:02:59Z)
• Joint Salient Object Detection and Camouflaged Object Detection via Uncertainty-aware Learning [47.253370009231645]
  We introduce an uncertainty-aware learning pipeline to explore the contradictory information of salient object detection (SOD) and camouflaged object detection (COD) Our solution leads to both state-of-the-art performance and informative uncertainty estimation.
  arXiv  Detail & Related papers  (2023-07-10T15:49:37Z)
• SOOD: Towards Semi-Supervised Oriented Object Detection [57.05141794402972]
  This paper proposes a novel Semi-supervised Oriented Object Detection model, termed SOOD, built upon the mainstream pseudo-labeling framework. Our experiments show that when trained with the two proposed losses, SOOD surpasses the state-of-the-art SSOD methods under various settings on the DOTA-v1.5 benchmark.
  arXiv  Detail & Related papers  (2023-04-10T11:10:42Z)
• DOAD: Decoupled One Stage Action Detection Network [77.14883592642782]
  Localizing people and recognizing their actions from videos is a challenging task towards high-level video understanding. Existing methods are mostly two-stage based, with one stage for person bounding box generation and the other stage for action recognition. We present a decoupled one-stage network dubbed DOAD, to improve the efficiency for-temporal action detection.
  arXiv  Detail & Related papers  (2023-04-01T08:06:43Z)
• Spatio-Temporal Relation Learning for Video Anomaly Detection [35.59510027883497]
  Anomaly identification is highly dependent on the relationship between the object and the scene. In this paper, we propose a Spatial-Temporal Relation Learning framework to tackle the video anomaly detection task. Experiments are conducted on three public datasets, and the superior performance over the state-of-the-art methods demonstrates the effectiveness of our method.
  arXiv  Detail & Related papers  (2022-09-27T02:19:31Z)
• Exploring Motion and Appearance Information for Temporal Sentence Grounding [52.01687915910648]
  We propose a Motion-Appearance Reasoning Network (MARN) to solve temporal sentence grounding. We develop separate motion and appearance branches to learn motion-guided and appearance-guided object relations. Our proposed MARN significantly outperforms previous state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2022-01-03T02:44:18Z)
• Triggering Failures: Out-Of-Distribution detection by learning from local adversarial attacks in Semantic Segmentation [76.2621758731288]
  We tackle the detection of out-of-distribution (OOD) objects in semantic segmentation. Our main contribution is a new OOD detection architecture called ObsNet associated with a dedicated training scheme based on Local Adversarial Attacks (LAA) We show it obtains top performances both in speed and accuracy when compared to ten recent methods of the literature on three different datasets.
  arXiv  Detail & Related papers  (2021-08-03T17:09:56Z)
• TRiPOD: Human Trajectory and Pose Dynamics Forecasting in the Wild [77.59069361196404]
  TRiPOD is a novel method for predicting body dynamics based on graph attentional networks. To incorporate a real-world challenge, we learn an indicator representing whether an estimated body joint is visible/invisible at each frame. Our evaluation shows that TRiPOD outperforms all prior work and state-of-the-art specifically designed for each of the trajectory and pose forecasting tasks.
  arXiv  Detail & Related papers  (2021-04-08T20:01:00Z)
• Uncertainty-aware Joint Salient Object and Camouflaged Object Detection [43.01556978979627]
  We propose a paradigm of leveraging the contradictory information to enhance the detection ability of both salient object detection and camouflaged object detection. We introduce a similarity measure module to explicitly model the contradicting attributes of these two tasks. Considering the uncertainty of labeling in both tasks' datasets, we propose an adversarial learning network to achieve both higher order similarity measure and network confidence estimation.
  arXiv  Detail & Related papers  (2021-04-06T16:05:10Z)
• Multi-object Tracking with a Hierarchical Single-branch Network [31.680667324595557]
  We propose an online multi-object tracking framework based on a hierarchical single-branch network. Our novel iHOIM loss function unifies the objectives of the two sub-tasks and encourages better detection performance. Experimental results on MOT16 and MOT20 datasets show that we can achieve state-of-the-art tracking performance.
  arXiv  Detail & Related papers  (2021-01-06T12:14:58Z)

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.