PaperlayerView exact revision on arXiv

Source-linked AI summary

Attribute-Centric Vision-Language Alignment for Fine-Grained Gait Understanding

Saihui Hou, Chenye Wang, Wenpeng Lang, Qiannan Guo, Zhengxiang Lan, Chao Fan, Shiqi Yu, Yongzhen Huang

arXiv:2610.14589v1cs.CV

TL;DR

Fine-grained gait understanding is constrained by limited attribute annotations and methods that typically use learned classifier weights rather than textual class representations. This paper introduces RA-GAR and Gait-VL, aligning attribute-specific multi-frame visual evidence with textual class prototypes. It reports state-of-the-art attribute recognition on RA-GAR and consistent identity-recognition improvements across evaluated downstream pipelines and benchmarks.

  • Problem

    Existing gait-attribute benchmarks provide limited fine-grained annotations, and typical classifier-based methods do not explicitly use textual class definitions as class representations.

    Source: §I · p. 1

  • Method

    RA-GAR supplies multimodal gait-attribute annotations, while Gait-VL matches attribute-specific visual evidence from RGB sequences with textual class prototypes.

    Source: §I · p. 2

  • Results

    Gait-VL achieves state-of-the-art attribute recognition on RA-GAR, and attribute pre-training consistently improves identity recognition across evaluated downstream pipelines and benchmarks.

    Source: §VII · p. 16

  • Takeaways & Limitations

    Attribute pre-training strengthens identity-recognition representations without identity classification or metric-learning objectives during pre-training.

    Source: §VII · p. 16

    The evaluation covers the attributes and collection settings represented in RA-GAR; future extensions could broaden walking patterns, environments, and participant populations.

    Source: D. Limitations and Future Directions · p. 15

Abstract

from arXiv · show

Walking sequences reveal human-interpretable attributes related to body structure, posture, and motion. Gait attribute recognition provides a concrete route to fine-grained gait understanding by predicting these attributes. However, existing benchmarks provide limited fine-grained annotations, while classifier-based gait attribute recognition methods typically represent candidate classes through learned output weights rather than directly matching visual evidence with textual class representations. To address the annotation gap, we present RA-GAR, a large-scale, richly annotated multimodal benchmark for gait attribute recognition, containing 1,001 subjects, 242,729 walking sequences, and annotations for 15 static and motion gait attributes. We further propose Gait-VL, an attribute-centric vision-language alignment framework that recognizes gait attributes by matching attribute-specific multi-frame visual evidence with textual class prototypes. On the visual side, attribute-specific learnable queries aggregate RGB features across spatial locations and frames into per-attribute visual embeddings. On the language side, diverse descriptions expanded by a large language model are combined with context about relevant body regions to construct textual class prototypes. Beyond attribute recognition, we evaluate the transferability of the attribute-pretrained visual encoder by using it to replace generic upstream large vision models in identityoriented gait recognition pipelines; attribute pre-training uses no identity classification or metric-learning objectives. Experiments demonstrate state-of-the-art attribute recognition performance on RA-GAR and consistent improvements in downstream identity recognition across multiple pipelines and benchmarks, supporting the effectiveness and transferability of attribute-centric gait representation learning.

I. INTRODUCTION

Fine-grained gait attributes offer human-readable descriptions of body structure, posture, and motion, but existing benchmarks and classifier-based methods leave gaps in annotation breadth and explicit language-visual matching. The paper introduces RA-GAR and Gait-VL, and examines whether attribute-level semantic supervision transfers to identity-oriented gait recognition.

I. INTRODUCTION

  • Existing dedicated benchmarks offer limited scale and fine-grained coverage, while typical attribute classifiers represent candidate classes with learned weights rather than textual definitions.MA-Gait has 13,954 sequences and 12 binary gait attributes; other gait datasets mainly provide coarse annotations or target adjacent problems.Source: §I · p. 1
  • RA-GAR contains 1,001 subjects and 242,729 sequences annotated for 15 static and motion attributes, spanning 7 binary and 8 multi-class attributes with 45 candidate labels.It provides RGB, silhouette, and 2D/3D pose modalities under a shared annotation system, with bag carrying and clothing changes represented as covariates.Source: §I · p. 2
  • Gait-VL formulates attribute recognition as sequence-text matching, aligning attribute-specific visual evidence with textual prototypes built from expanded descriptions and body-region context.Learnable queries aggregate RGB features across spatial locations and frames into per-attribute embeddings.Source: §I · p. 2
  • Attribute-pretrained visual features improve downstream identity recognition when substituted for generic upstream large vision models in existing gait pipelines, without identity classification or metric-learning objectives during pre-training.The downstream architectures, objectives, and evaluation protocols remain unchanged.Source: §I · p. 2

A. Gait Datasets

  • Prior gait datasets primarily target identity recognition or narrower semantic tasks, and none combines a large population, broad static and motion attributes, multiple modalities, and fine-grained sequence-level annotations.RA-GAR makes fine-grained static and motion attributes its primary annotation target across RGB, silhouette, and pose sequences.Source: A. Gait Datasets · p. 3

B. Gait Attribute Recognition

Prior work covers gait attributes, identity recognition, and vision-language or motion pre-training, but fine-grained gait semantics remain distinct from those works’ primary targets. RA-GAR defines a broad attribute taxonomy and annotation rules, while the related approaches motivate attribute-specific sequence-level supervision.

B. Gait Attribute Recognition

  • Gait attribute recognition has progressed from coarse cues to multi-attribute classification, while pedestrian attribute methods offer localization, temporal aggregation, and vision-language approaches for appearance-oriented evidence.GAR-Net represents candidate classes through learned output weights rather than explicit textual class definitions.Source: B. Gait Attribute Recognition · p. 3

C. Gait Identity Recognition

  • Identity-oriented gait methods use pose, silhouette, or RGB representations but are primarily optimized for identity discrimination; related ReID work typically combines attributes with identity objectives rather than isolating attribute-only pre-training.The paper evaluates transfer after pre-training the visual encoder with attribute objectives alone.Source: C. Gait Identity Recognition · p. 3

D. Vision-Language Learning and Gait Pre-training

  • Image-text, video, motion, and person-ReID pre-training provide relevant foundations, but the cited approaches do not use fine-grained gait attribute descriptions as sequence-level prediction targets.Gait-language methods described here focus on identity-oriented representations rather than attribute prediction.Source: D. Vision-Language Learning and Gait… · p. 4

III. DATASETS

  • RA-GAR makes semantic walking attributes the primary target in a 1,001-subject, 242,729-sequence benchmark spanning 15 static and motion attributes and varied acquisition conditions.The dataset includes bag carrying and clothing changes as walking covariates.Source: §III · p. 4

A. Gait Attribute Definition

  • RA-GAR defines attributes as semantically clear, gait-relevant, observable concepts spanning static characteristics and dynamic patterns, organized by semantic type and primary body-region cues.Its taxonomy has 15 attributes: 7 binary and 8 multi-class; selected labels use explicit measurement or visible-state rules, including a 30° arm-swing threshold.Source: A. Gait Attribute Definition · pp. 4–5

B. Data Collection

RA-GAR combines two acquisition scenarios with shared gait-attribute annotations, multiple data modalities, and walking-condition variation. It retains a subject-disjoint test split and provides request-based, research-only access.

B. Data Collection

  • Scenario 1 uses five cameras for 10 effective views, while Scenario 2 uses four cameras for eight effective views under different capture conditions.The collection includes natural and instructed attribute states, with walks under normal conditions, bag carrying, and clothing changes.Source: B. Data Collection · p. 5

C. Data Processing and Statistics

  • After quality filtering, RA-GAR contains 1,001 subjects and 242,729 sequences annotated for 15 attributes; RGB, silhouettes, and 2D/3D poses share the annotations.Several fine-grained attributes remain imbalanced, and evaluation uses attribute-based mean accuracy alongside standard instance-based metrics.Source: C. Data Processing and Statistics · p. 6

D. Dataset Split and Evaluation Settings

  • The subject-disjoint split assigns 500 subjects to training and 501 to testing, with the full test set primary and scenario-specific results reported separately.The same protocol supports classifier-based prediction, text-guided sequence-text matching, and evaluation of attribute pre-training for identity recognition.Source: D. Dataset Split and Evaluation Settings · p. 6

E. Consent, Privacy, and Data Access

  • Participants or guardians provide written consent, and RA-GAR uses pseudonymous identifiers without direct administrative identifiers.Access is request-based and restricted to approved research use; data-use terms prohibit re-identification attempts and unauthorized redistribution.Source: E. Consent, Privacy, and Data Access · p. 6

IV. METHODS

Gait-VL frames gait attribute recognition as matching RGB walking sequences to textual class semantics rather than relying on learned classifier weights. It aggregates attribute-specific visual evidence across frames and matches it to prototypes built from class descriptions.

IV. METHODS

  • Gait-VL recognizes attributes by aligning RGB gait sequences with textual class semantics and uses the resulting visual encoder for downstream gait recognition without identity-level pre-training losses.The framework specifies a sequence-text matching interface for its attribute predictions.Source: §IV · pp. 6–7

A. Problem Formulation

  • Instead of conventional classification heads, the formulation compares each gait sequence with textual descriptions of predefined candidate labels.The candidate-label set is defined for each attribute; examples include toe orientation and limping states.Source: A. Problem Formulation · p. 7

B. Overview of Gait-VL

  • Attribute-specific temporal and spatial queries aggregate multi-frame visual evidence into per-attribute embeddings, which are matched to prototypes built from banks of class descriptions.Training samples stochastic prototypes from descriptions for semantic robustness, while inference uses deterministic prototypes aggregated from the complete description bank.Source: B. Overview of Gait-VL · p. 7

C. Gait-Centric Vision-Language Backbone

Gait-VL uses an image-text backbone suited to portrait pedestrian crops and multi-frame encoding. Its sequence encoder preserves spatial tokens, aggregates frame evidence with attribute-specific temporal queries, and prepares attribute-specific features for spatial pooling.

C. Gait-Centric Vision-Language Backbone

  • SigLIP2-Base-Patch16-NaFlex supplies a shared image-text space, supports varying aspect ratios and spatial token grids, and uses a compact configuration to limit multi-frame encoding costs.Frames are resized to a fixed portrait resolution for a consistent spatial token grid.Source: C. Gait-Centric Vision-Language Backbone · pp. 7–8

1) Frame-Level Token Representation:

  • Each RGB frame is encoded into retained spatial tokens, preserving body-localized evidence such as cues from the head, shoulders, arms, or feet.The encoder avoids image-level pooling before the sequence aggregation stages.Source: 1) Frame-Level Token Representation · p. 8

2) Per-Attribute Temporal Aggregation:

  • Attribute-specific temporal queries attend across frames at each spatial position, then a residual temporal mean and learnable channel-wise scale produce an attribute-specific token map.Unlike uniform mean pooling, learnable queries assign attribute-specific weights to observed frame features.Source: 2) Per-Attribute Temporal Aggregation · p. 8

3) Per-Attribute Spatial Aggregation:

Gait-VL pools each attribute’s temporally aggregated tokens into a visual embedding and constructs text embedding banks from concise label seeds and expanded, gait-aware descriptions.

3) Per-Attribute Spatial Aggregation:

E. Attribute Text Encoder

  • Each attribute-label pair begins with a grammatical template seed that anchors its annotation semantics, such as “The hair of this pedestrian is long.”The seeds use attribute-specific nouns, copulas, and predicates.Source: E. Attribute Text Encoder · p. 9
  • Qwen3.5-Plus expands each seed into distinct descriptions with attribute-specific body-region context, and the descriptions are manually reviewed for label consistency.The frozen text encoder caches the resulting embeddings, stabilizing the language space and avoiding repeated encoding.Source: E. Attribute Text Encoder · p. 9

F. Attribute-Centric Sequence-Text Matching

Training matches attribute-specific visual embeddings to stochastic prototypes sampled from cached descriptions. A class-wise sigmoid loss and an attribute-wise cross-entropy loss jointly train the alignment.

F. Attribute-Centric Sequence-Text Matching

1) Training Objectives:

  • The class-wise sigmoid loss learns an absolute decision boundary for each candidate label, while attribute-wise cross-entropy encourages relative separation among labels of the same attribute.The final objective combines both terms with unit coefficients.Source: 1) Training Objectives · p. 10

2) Text-Guided Attribute Inference:

Inference averages each label’s complete text embedding bank into a deterministic prototype for attribute prediction. Separately, the paper tests attribute-pretrained visual encoders in identity-recognition pipelines while leaving their downstream components and evaluation protocols unchanged.

2) Text-Guided Attribute Inference:

  • At inference, each label’s complete embedding bank is averaged into a deterministic prototype, and candidate-label logits are thresholded independently at zero without a within-attribute argmax.The independent threshold follows the unified binary-column evaluation protocol.Source: 2) Text-Guided Attribute Inference · p. 10

G. Attribute Pre-training for Gait Recognition

  • The transfer protocol replaces a generic upstream visual encoder with the attribute-pretrained encoder while keeping downstream mappings, identity objectives, gallery/probe protocols, and metrics unchanged.Attribute pre-training uses sequence-text matching, not identity classification or metric-learning losses; identity labels do not enter its objective.Source: G. Attribute Pre-training for Gait… · pp. 10–11

V. EXPERIMENTS

The experiment evaluates all 15 RA-GAR attributes on a shared split, using the full test set as primary and scenario subsets as additional evaluations.

1) Gait Attribute Recognition:

  • The full RA-GAR test set is the primary evaluation set, with separate runs on both scenario subsets using the same checkpoint and no subset-specific tuning.All methods are evaluated on the same 15 annotated attributes under the Section III-D split.Source: 1) Gait Attribute Recognition · p. 11
  • Gait-VL (Zero-Shot) is an evaluation-only sequence adaptation of pretrained SigLIP2 without RA-GAR optimization.It uses the temporal-before-spatial organization and LLM-expanded description banks encoded by the pretrained SigLIP2 text encoder.Source: 1) Gait Attribute Recognition · p. 11
  • Evaluation reports attribute-based mean accuracy (mA), instance accuracy (Acc.), and instance F1 over 45 candidate-label columns thresholded at zero.The zero logit threshold corresponds to a sigmoid probability threshold of 0.5.Source: 1) Gait Attribute Recognition · p. 11

2) Attribute Pre-training Evaluation:

Gait-VL leads the full-set attribute-recognition comparison and retains advantages across acquisition scenarios; attribute-pretrained representations also improve identity recognition in unchanged downstream pipelines.

2) Attribute Pre-training Evaluation:

  • Identity evaluation follows BigGait and BiggerGait protocols on CCPG, SUSTech1K, and CCGR-MINI, with Rank-1 as the primary metric and Rank-5 also reported on CCGR-MINI.Gait-VL (Zero-Shot) denotes the upstream representation without RA-GAR optimization, not an absence of downstream identity training.Source: 2) Attribute Pre-training Evaluation · p. 11

1) Gait Attribute Recognition:

2) Performance by Acquisition Scenario:

  • Gait-VL leads on F1 and mA in both scenarios, but its advantage shrinks in Scenario 2, where pose and silhouette methods score higher while generic RGB methods score lower.Gait-VL also leads Acc. in Scenario 1 and ties GaitGL for the highest Acc. in Scenario 2.Source: Table IV · p. 12, 2) Performance by Acquisition Scenario · p. 13

1) Gait Attribute Recognition:

  • Gait-VL achieves the best result on 12 of 15 attributes, while CLIP-GAR remains stronger on Body mass, Toe, and Limping.Relative to CLIP-GAR, gains are 7.36 percentage points on Hands and 5.42 percentage points on Upper arm movement.Source: Table V · p. 12, 1) Gait Attribute Recognition · p. 13

2) Attribute Pre-training Evaluation:

  • Attribute-pretrained visual encoders improve downstream identity-recognition results without changing downstream architectures or hyperparameters.Attribute pre-training uses RA-GAR as a semantic corpus without identity classification or metric-learning objectives; RA-GAR shares no subjects with the evaluated identity benchmarks.Source: B. Implementation Details · p. 12, 3) Attribute Pre-training Evaluation · p. 13

D. Ablation Studies

The ablations favor portrait inputs, attribute-specific queries at both aggregation stages, joint training objectives, and expanded gait descriptions; the resulting visual encoder adds modest computation.

1) Effect of Input Resolution and Model Scale:

  • At matched heights, portrait inputs outperform square inputs on all three metrics, and 448 × 224 is best or tied-best; raising portrait resolution to 512×256 adds no improvement.With the input fixed at 448×224, SO400M slightly raises Acc. over SigLIP2 Base, but neither F1 nor mA improves.Source: Table VII · p. 14, 1) Effect of Input Resolution and… · p. 14

2) Effect of Attribute-Aware Sequence Encoding:

  • Attribute-specific queries in both temporal and spatial stages produce the best results, improving mA by 3.13 percentage points over uniform mean aggregation.Temporal-before-spatial aggregation also outperforms the reversed order on all three metrics.Source: 2) Effect of Attribute-Aware Sequence… · p. 14

3) Effect of Training Objectives:

  • Combining class-wise sigmoid loss with attribute-wise softmax cross-entropy improves all three metrics over sigmoid-only training and yields the best results.Softmax cross-entropy alone performs substantially worse, particularly in F1.Source: 3) Effect of Training Objectives · p. 14

4) Effect of Attribute Text Construction:

  • Sentence-level templates improve all three metrics over attribute–label strings, and LLM-expanded descriptions yield further gains, raising mA by 2.74 percentage points in the full setting.The expanded banks include linguistic diversity and context about relevant body regions.Source: 4) Effect of Attribute Text Construction · p. 14

5) Computational Cost Analysis:

  • At 448 × 224, Gait-VL's visual pathway uses 95.32M parameters and 74.36 GFLOPs per frame, increases of approximately 2.6% and 2.2% over SigLIP2, respectively.The comparison includes only the visual pathway.Source: 5) Computational Cost Analysis · p. 14

VI. DISCUSSION AND FUTURE WORK

The discussion frames gait attributes as targets for fine-grained understanding and as supervision for identity representations, while noting practical safeguards and limits in benchmark coverage.

A. Attribute-Level Gait Understanding

  • RA-GAR supplies shared definitions, annotations, and evaluation protocols for studying recognition of body-structure, posture, and motion attributes across its represented acquisition conditions.Gait-VL aligns attribute-specific visual representations with textual prototypes, incorporating human-readable class definitions into representation learning and prediction.Source: A. Attribute-Level Gait Understanding · p. 15

B. Attribute Semantics as a Bridge to Gait Recognition

  • Across evaluated protocols and both identity-recognition pipelines, attribute-pretrained Gait-VL improves over the same pretrained backbone without RA-GAR attribute training, despite using no identity or metric-learning objectives.The paper presents gait attributes as both fine-grained recognition targets and transferable supervision.Source: B. Attribute Semantics as a Bridge to… · p. 15

C. Practical Implications and Responsible Use

  • Attribute predictions could support clip retrieval, comparative analysis, concise summaries, and interpretable error analysis, but these proposed applications require evaluation in their intended settings.For individual cases, summaries should be treated as model estimates and reviewed alongside the original sequences; dataset use should follow consent and controlled-access terms.Source: C. Practical Implications and… · p. 15

D. Limitations and Future Directions

  • Current evaluation is bounded by RA-GAR's attributes and collection settings; broader walking patterns, environments, participant populations, and attribute sets are future extensions.The authors also propose exploring text-conditioned visual queries to select relevant body regions and cues.Source: D. Limitations and Future Directions · p. 15

VII. CONCLUSION

  • Gait-VL achieves state-of-the-art attribute recognition on RA-GAR, while attribute pre-training consistently improves identity recognition across evaluated pipelines and benchmarks without identity or metric-learning objectives.The conclusion connects human-interpretable gait semantics with discriminative representation learning.Source: §VII · pp. 15–16

Related papers

Loading 2610.14589v1…