HAMoBE: Hierarchical and Adaptive Mixture of Biometric Experts for Video-based Person ReID

• URL: http://arxiv.org/abs/2508.05038v2
• Date: Wed, 27 Aug 2025 04:31:10 GMT
• Title: HAMoBE: Hierarchical and Adaptive Mixture of Biometric Experts for Video-based Person ReID
• Abstract summary: We propose a novel Hierarchical and Adaptive Mixture of Biometric Experts (HAMoBE) framework.<n>HamoBE mimics human perceptual mechanisms by independently modeling key biometric features.<n>We show that our approach yields significant performance improvements.
• Score: 14.923830228090246
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recently, research interest in person re-identification (ReID) has increasingly focused on video-based scenarios, which are essential for robust surveillance and security in varied and dynamic environments. However, existing video-based ReID methods often overlook the necessity of identifying and selecting the most discriminative features from both videos in a query-gallery pair for effective matching. To address this issue, we propose a novel Hierarchical and Adaptive Mixture of Biometric Experts (HAMoBE) framework, which leverages multi-layer features from a pre-trained large model (e.g., CLIP) and is designed to mimic human perceptual mechanisms by independently modeling key biometric features--appearance, static body shape, and dynamic gait--and adaptively integrating them. Specifically, HAMoBE includes two levels: the first level extracts low-level features from multi-layer representations provided by the frozen large model, while the second level consists of specialized experts focusing on long-term, short-term, and temporal features. To ensure robust matching, we introduce a new dual-input decision gating network that dynamically adjusts the contributions of each expert based on their relevance to the input scenarios. Extensive evaluations on benchmarks like MEVID demonstrate that our approach yields significant performance improvements (e.g., +13.0% Rank-1 accuracy).

Related papers

• Gait Recognition via Deep Residual Networks and Multi-Branch Feature Fusion [5.585970057888865]
  We present a high-precision gait recognition framework that deeply extracts and fuses gait dynamics with body shape characteristics.<n>A 50-layer Residual Network is leveraged within a deep feature extraction module to capture hierarchically rich and discriminative representations.<n>Experiments on the cross-view multi-condition CASIA-B benchmark demonstrate that our method achieves a Rank-1 accuracy of 94.52% under normal walking.
  arXiv  Detail & Related papers  (2026-04-30T03:09:51Z)
• Beyond Pedestrians: Caption-Guided CLIP Framework for High-Difficulty Video-based Person Re-Identification [0.0]
  We propose a caption-guided CLIP framework that leverages textual descriptions and learnable tokens.<n>We evaluate our approach on two standard datasets and two newly constructed high-difficulty datasets.<n> Experimental results demonstrate that our method outperforms current state-of-art approaches.
  arXiv  Detail & Related papers  (2026-04-09T02:55:51Z)
• Video-MME-v2: Towards the Next Stage in Benchmarks for Comprehensive Video Understanding [98.3098451637867]
  Video-MME-v2 is a comprehensive benchmark designed to rigorously evaluate the robustness and faithfulness of video understanding.<n>To guarantee data quality, Video-MME-v2 is constructed through a rigorously controlled human annotation pipeline.
  arXiv  Detail & Related papers  (2026-04-06T17:59:56Z)
• PEARL: Personalized Streaming Video Understanding Model [50.273809779498464]
  multimodal personalization methods are largely limited to static images or offline videos.<n>PEARL-Bench is the first comprehensive benchmark designed specifically to evaluate this challenging setting.<n>PEARL achieves state-of-the-art performance across 8 offline and online models.
  arXiv  Detail & Related papers  (2026-03-20T18:47:54Z)
• MOSIV: Multi-Object System Identification from Videos [54.53609234233857]
  We propose a new framework that directly optimize for continuous, per-object material parameters using a differentiable simulator guided by geometric objectives derived from video.<n>We also present a new synthetic benchmark with contact-rich, multi-object interactions to facilitate evaluation.
  arXiv  Detail & Related papers  (2026-03-06T08:16:16Z)
• MSVBench: Towards Human-Level Evaluation of Multi-Shot Video Generation [48.84450712826316]
  MSVBench is the first comprehensive benchmark featuring hierarchical scripts and reference images tailored for Multi-Shot Video generation.<n>We propose a hybrid evaluation framework that synergizes the high-level semantic reasoning of Large Multimodal Models with the fine-grained perceptual rigor of domain-specific expert models.
  arXiv  Detail & Related papers  (2026-02-27T12:26:34Z)
• Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts [10.204413386807564]
  We propose a Dynamic Adaptive Shared Expert and Grouped Multi-Head Attention Hybrid Model (DASG-MoE) to enhance long-sequence modeling capabilities.<n>First, we employ the Grouped Multi-Head Attention (GMHA) mechanism to effectively reduce the computational complexity of long sequences.<n>Second, we design a Dual-Scale Shared Expert Structure (DSSE), where shallow experts use lightweight computations to quickly respond to low-dimensional features.<n>Third, we propose a hierarchical Adaptive Dynamic Routing (ADR) mechanism that dynamically selects expert levels based on feature complexity and task requirements.
  arXiv  Detail & Related papers  (2025-09-05T02:49:15Z)
• HV-MMBench: Benchmarking MLLMs for Human-Centric Video Understanding [79.06209664703258]
  Multimodal Large Language Models (MLLMs) have demonstrated significant advances in visual understanding tasks involving both images and videos.<n>Existing human-centric benchmarks predominantly emphasize video generation quality and action recognition, while overlooking essential perceptual and cognitive abilities required in human-centered scenarios.<n>We propose a rigorously curated benchmark designed to provide a more holistic evaluation of MLLMs in human-centric video understanding.
  arXiv  Detail & Related papers  (2025-07-07T11:52:24Z)
• MemoryOut: Learning Principal Features via Multimodal Sparse Filtering Network for Semi-supervised Video Anomaly Detection [30.470777079947958]
  Video Anomaly Detection (VAD) methods based on reconstruction or prediction face two critical challenges.<n>Strong generalization capability often results in accurate reconstruction or prediction of abnormal events.<n>reliance only on low-level appearance and motion cues limits their ability to identify high-level semantic in abnormal events from complex scenes.
  arXiv  Detail & Related papers  (2025-06-03T07:14:57Z)
• ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding [71.654781631463]
  ReAgent-V is a novel agentic video understanding framework.<n>It integrates efficient frame selection with real-time reward generation during inference.<n>Extensive experiments on 12 datasets demonstrate significant gains in generalization and reasoning.
  arXiv  Detail & Related papers  (2025-06-02T04:23:21Z)
• SapiensID: Foundation for Human Recognition [15.65725865703615]
  SapiensID is a unified model for face and body analysis, achieving robust performance across diverse settings.<n>To facilitate training, we introduce WebBody4M, a large-scale dataset capturing diverse poses and scale variations.<n>Experiments demonstrate that SapiensID achieves state-of-the-art results on various body ReID benchmarks, outperforming specialized models in both short-term and long-term scenarios.
  arXiv  Detail & Related papers  (2025-04-07T03:38:07Z)
• Astrea: A MOE-based Visual Understanding Model with Progressive Alignment [10.943104653307294]
  Vision-Language Models (VLMs) based on Mixture-of-Experts (MoE) architectures have emerged as a pivotal paradigm in multimodal understanding.<n>We propose Astrea, a novel multi-expert collaborative VLM architecture based on progressive pre-alignment.
  arXiv  Detail & Related papers  (2025-03-12T14:44:52Z)
• Exploiting Modality-Specific Features For Multi-Modal Manipulation Detection And Grounding [54.49214267905562]
  We construct a transformer-based framework for multi-modal manipulation detection and grounding tasks. Our framework simultaneously explores modality-specific features while preserving the capability for multi-modal alignment. We propose an implicit manipulation query (IMQ) that adaptively aggregates global contextual cues within each modality.
  arXiv  Detail & Related papers  (2023-09-22T06:55:41Z)
• Multi-Frame Self-Supervised Depth Estimation with Multi-Scale Feature Fusion in Dynamic Scenes [25.712707161201802]
  Multi-frame methods improve monocular depth estimation over single-frame approaches. Recent methods tend to propose complex architectures for feature matching and dynamic scenes. We show that a simple learning framework, together with designed feature augmentation, leads to superior performance.
  arXiv  Detail & Related papers  (2023-03-26T05:26:30Z)
• Multimodal Adaptive Fusion of Face and Gait Features using Keyless attention based Deep Neural Networks for Human Identification [67.64124512185087]
  Soft biometrics such as gait are widely used with face in surveillance tasks like person recognition and re-identification. We propose a novel adaptive multi-biometric fusion strategy for the dynamic incorporation of gait and face biometric cues by leveraging keyless attention deep neural networks.
  arXiv  Detail & Related papers  (2023-03-24T05:28:35Z)
• Multi-Stage Spatio-Temporal Aggregation Transformer for Video Person Re-identification [78.08536797239893]
  We propose a novel Multi-Stage Spatial-Temporal Aggregation Transformer (MSTAT) with two novel designed proxy embedding modules. MSTAT consists of three stages to encode the attribute-associated, the identity-associated, and the attribute-identity-associated information from the video clips. We show that MSTAT can achieve state-of-the-art accuracies on various standard benchmarks.
  arXiv  Detail & Related papers  (2023-01-02T05:17:31Z)
• MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection [76.80153360498797]
  We develop a multiple instance self-training framework (MIST) to efficiently refine task-specific discriminative representations. MIST is composed of 1) a multiple instance pseudo label generator, which adapts a sparse continuous sampling strategy to produce more reliable clip-level pseudo labels, and 2) a self-guided attention boosted feature encoder. Our method performs comparably to or even better than existing supervised and weakly supervised methods, specifically obtaining a frame-level AUC 94.83% on ShanghaiTech.
  arXiv  Detail & Related papers  (2021-04-04T15:47:14Z)
• Depth Guided Adaptive Meta-Fusion Network for Few-shot Video Recognition [86.31412529187243]
  Few-shot video recognition aims at learning new actions with only very few labeled samples. We propose a depth guided Adaptive Meta-Fusion Network for few-shot video recognition which is termed as AMeFu-Net.
  arXiv  Detail & Related papers  (2020-10-20T03:06:20Z)

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.