Learning A Unified Template for Gait Recognition

arXiv:2609.18490 · cs.CV · Submitted 2026-09-16 · Read on arXiv

cs.CV

Submitted: 2026-09-16

Updated: 2026-09-16

Terminology

• Diffusion Model — 27× in this paper · explained in 8 episodes
• Diffusion Models — 23× in this paper · explained in 6 episodes
• Representation Learning — 22× in this paper · explained in 2 episodes
• Accuracy — 13× in this paper · explained in 1 episode
• Benchmark — 9× in this paper · explained in 2 episodes
• Attention Mechanism — 5× in this paper · explained in 2 episodes
• Transformer — 5× in this paper · explained in 2 episodes
• Core — 4× in this paper · explained in 2 episodes
• Diffusion Process — 3× in this paper · explained in 2 episodes
• Horizontal Partition — 3× in this paper
• Recognition Head — 3× in this paper
• Robustness — 3× in this paper · explained in 5 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Latent Diffusion Models — 2× in this paper · explained in 2 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Point Clouds — 2× in this paper · explained in 2 episodes
• Unified Framework — 2× in this paper · explained in 5 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Diffusion Processes — 1× in this paper · explained in 2 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Mixture of Experts — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Self-Supervised Learning — 1× in this paper · explained in 7 episodes
• U-net architecture — 1× in this paper · explained in 3 episodes

Sources

• Can Generative Models Improve Self-Supervised Representation Learning?
• Label-Efficient Semantic Segmentation with Diffusion Models
• Deconstructing Denoising Diffusion Models for Self-Supervised Learning
• In Defense of the Triplet Loss for Person Re-Identification
• Classifier-Free Diffusion Guidance
• Return of Unconditional Generation: A Self-supervised Representation Generation Method
• GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
• ADDP: Learning General Representations for Image Recognition and Generation with Alternating Denoising Diffusion Process
• HiH: A Multi-modal Hierarchy in Hierarchy Network for Unconstrained Gait Recognition
• Your ViT is Secretly a Hybrid Discriminative-Generative Diffusion Model
• Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think

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