arXiv Computer Vision
Sep 17

Vocabulary-Guided Gait Recognition

The paper introduces Vocabulary‑Guided Gait Recognition (Gait‑World), a new paradigm that leverages human vocabularies and Vision‑Language Models (VLMs) to better understand and recognize gait. It proposes the α‑Gait model, which uses a Vocabulary Relation Mapper and a Gait Fine‑grained Detector to align vocabulary prompts with gait features, addressing the challenge of heterogeneous embedding spaces. Experiments on multiple gait datasets demonstrate the potential of incorporating VLM‑derived vocabulary information into gait recognition.

By Panjian Huang, Saihui Hou, Chunshui Cao, Xu Liu, Yongzhen Huang
arXiv Computer Vision
Aug 26

Representation Learning in Diffusion and Flow-based Model: An Application Aspect

The article surveys how diffusion and flow-based generative models learn rich visual representations and how these representations can be used to improve generation and other perception tasks. It introduces a three-tier framework that categorizes work into improving generative quality via representation learning, extracting representations for perception, and developing unified applications. The survey covers downstream tasks such as image classification, dense prediction, instance-level perception, and annotation-scarce scenarios, offering a taxonomy and highlighting future research directions.

By Yanchen Xu, Sida Huang, Zhenyu Gu, Ruishu Zhu, Yilan Gao, Hongyuan Zhang
arXiv Machine Learning
Oct 15

Modeling and Understanding Neural Weights of Humanoid Policies with Diffusion

The paper presents a latent diffusion framework that models the distribution of neural network weights for humanoid control policies. By compressing actor network parameters with a linear autoencoder and modeling the latent space with a Diffusion Transformer, the authors generate deployable networks that perform real‑world motions such as walking, dancing, and spin kicks on the Unitree G1 robot. The study also reveals a structural principle in the first‑layer weights that reflects the robot’s kinematic prior, appearing consistently across both reinforcement and imitation learning training methods.

By Huixuan Cheng, Bike Zhang, Shankar Sastry, Koushil Sreenath