BEAR: A Video Dataset For Fine-grained Behaviors Recognition Oriented with Action and Environment Factors

• URL: http://arxiv.org/abs/2503.20209v1
• Date: Wed, 26 Mar 2025 04:06:20 GMT
• Title: BEAR: A Video Dataset For Fine-grained Behaviors Recognition Oriented with Action and Environment Factors
• Authors: Chengyang Hu, Yuduo Chen, Lizhuang Ma,
• Abstract summary: We develop a new video fine-grained behavior dataset, named BEAR, which provides fine-grained (i.e. similar) behaviors.<n>It includes two fine-grained behavior protocols including Fine-grained Behavior with Similar Environments and Fine-grained Behavior with Similar Actions.<n>Our research primarily explores the impact of input modality, a critical element in studying the environmental and action-based aspects of behavior recognition.
• Score: 27.372230107619973
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Behavior recognition is an important task in video representation learning. An essential aspect pertains to effective feature learning conducive to behavior recognition. Recently, researchers have started to study fine-grained behavior recognition, which provides similar behaviors and encourages the model to concern with more details of behaviors with effective features for distinction. However, previous fine-grained behaviors limited themselves to controlling partial information to be similar, leading to an unfair and not comprehensive evaluation of existing works. In this work, we develop a new video fine-grained behavior dataset, named BEAR, which provides fine-grained (i.e. similar) behaviors that uniquely focus on two primary factors defining behavior: Environment and Action. It includes two fine-grained behavior protocols including Fine-grained Behavior with Similar Environments and Fine-grained Behavior with Similar Actions as well as multiple sub-protocols as different scenarios. Furthermore, with this new dataset, we conduct multiple experiments with different behavior recognition models. Our research primarily explores the impact of input modality, a critical element in studying the environmental and action-based aspects of behavior recognition. Our experimental results yield intriguing insights that have substantial implications for further research endeavors.

Related papers

• BEAC: Imitating Complex Exploration and Task-oriented Behaviors for Invisible Object Nonprehensile Manipulation [9.453992660558256]
  This paper proposes a novel imitation learning framework called Belief Exploration-Action Cloning (BEAC)<n>Our proposed method achieved the best task performance, higher mode and action prediction accuracies, while reducing the cognitive load in a user study.
  arXiv  Detail & Related papers  (2025-03-21T02:26:14Z)
• HabitAction: A Video Dataset for Human Habitual Behavior Recognition [3.7478789114676108]
  Human habitual behaviors (HHBs) hold significant importance for analyzing a person's personality, habits, and psychological changes. In this work, we build a novel video dataset to demonstrate various HHBs. The dataset contains 30 categories of habitual behaviors including more than 300,000 frames and 6,899 action instances.
  arXiv  Detail & Related papers  (2024-08-24T04:40:31Z)
• 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)
• Decoding Neural Activity to Assess Individual Latent State in Ecologically Valid Contexts [1.1059590443280727]
  We use data from two highly controlled laboratory paradigms to train two separate domain-generalized models. We derive estimates of the underlying latent state and associated patterns of neural activity.
  arXiv  Detail & Related papers  (2023-04-18T15:15:00Z)
• Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation [52.89816309759537]
  Multi-types of behaviors (e.g., clicking, adding to cart, purchasing, etc.) widely exist in most real-world recommendation scenarios. The state-of-the-art multi-behavior models learn behavior dependencies indistinguishably with all historical interactions as input. We propose a novel Coarse-to-fine Knowledge-enhanced Multi-interest Learning framework to learn shared and behavior-specific interests for different behaviors.
  arXiv  Detail & Related papers  (2022-08-03T05:28:14Z)
• Few-Shot Fine-Grained Action Recognition via Bidirectional Attention and Contrastive Meta-Learning [51.03781020616402]
  Fine-grained action recognition is attracting increasing attention due to the emerging demand of specific action understanding in real-world applications. We propose a few-shot fine-grained action recognition problem, aiming to recognize novel fine-grained actions with only few samples given for each class. Although progress has been made in coarse-grained actions, existing few-shot recognition methods encounter two issues handling fine-grained actions.
  arXiv  Detail & Related papers  (2021-08-15T02:21:01Z)
• Towards Unbiased Visual Emotion Recognition via Causal Intervention [63.74095927462]
  We propose a novel Emotion Recognition Network (IERN) to alleviate the negative effects brought by the dataset bias. A series of designed tests validate the effectiveness of IERN, and experiments on three emotion benchmarks demonstrate that IERN outperforms other state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-07-26T10:40:59Z)
• Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Framework [83.21732533130846]
  The paper focuses on large in-the-wild databases, i.e., Aff-Wild and Aff-Wild2. It presents the design of two classes of deep neural networks trained with these databases. A novel multi-task and holistic framework is presented which is able to jointly learn and effectively generalize and perform affect recognition.
  arXiv  Detail & Related papers  (2021-03-29T17:36:20Z)
• Disentangling causal effects for hierarchical reinforcement learning [0.0]
  This study aims to expedite the learning of task-specific behavior by leveraging a hierarchy of causal effects. We propose CEHRL, a hierarchical method that models the distribution of controllable effects using a Variational Autoencoder. In comparison to exploring with random actions, experimental results show that random effect exploration is a more efficient mechanism.
  arXiv  Detail & Related papers  (2020-10-03T13:19:16Z)
• Action similarity judgment based on kinematic primitives [48.99831733355487]
  We investigate to which extent a computational model based on kinematics can determine action similarity. The chosen model has its roots in developmental robotics and performs action classification based on learned kinematic primitives. The results show that both the model and human performance are highly accurate in an action similarity task based on kinematic-level features.
  arXiv  Detail & Related papers  (2020-08-30T13:58:47Z)
• ConvGRU in Fine-grained Pitching Action Recognition for Action Outcome Prediction [4.073910992747716]
  Fine-grained action recognition is significant in many fields such as human-robot interaction, intelligent traffic management, sports training, health caring. In this paper, we explore the performance of convolutional gate recurrent unit (ConvGRU) method on a fine-grained action recognition tasks. Based on sequences of RGB images of human actions, the proposed approach achieved the performance of 79.17% accuracy.
  arXiv  Detail & Related papers  (2020-08-18T09:27:17Z)

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.