23 citations · 32 across the 9 of their papers we have counts for
9 papers
Learning More with Less: A Dynamic Dual-Level Down-Sampling Framework for Efficient Policy Optimization
Chao Wang, Tao Yang, Hongtao Tian +5
Critic-free methods like GRPO reduce memory demands by estimating advantages from multiple rollouts but tend to converge slowly, as critical learning signals are diluted by an abun…
WeChat-YATT: A Scalable, Simple, Efficient, and Production Ready Training Library
Junyu Wu, Weiming Chang, Xiaotao Liu +10
Reinforcement Learning from Human Feedback (RLHF) has emerged as a prominent paradigm for training large language models and multimodal systems. Despite the notable advances enable…
G-Core: A Simple, Scalable and Balanced RLHF Trainer
Junyu Wu, Weiming Chang, Xiaotao Liu +8
Reinforcement Learning from Human Feedback (RLHF) has become an increasingly popular paradigm for training large language models (LLMs) and diffusion models. While existing RLHF tr…
Cross Fusion RGB-T Tracking with Bi-directional Adapter
Zhirong Zeng, Xiaotao Liu, Meng Sun +2
Many state-of-the-art RGB-T trackers have achieved remarkable results through modality fusion. However, these trackers often either overlook temporal information or fail to fully u…
Temporal Adaptive RGBT Tracking with Modality Prompt
Hongyu Wang, Xiaotao Liu, Yifan Li +3
RGBT tracking has been widely used in various fields such as robotics, surveillance processing, and autonomous driving. Existing RGBT trackers fully explore the spatial information…
Video Event Restoration Based on Keyframes for Video Anomaly Detection
Zhiwei Yang, Jing Liu, Zhaoyang Wu +2
Video anomaly detection (VAD) is a significant computer vision problem. Existing deep neural network (DNN) based VAD methods mostly follow the route of frame reconstruction or fram…
Dynamic Local Aggregation Network with Adaptive Clusterer for Anomaly Detection
Zhiwei Yang, Peng Wu, Jing Liu +1
Existing methods for anomaly detection based on memory-augmented autoencoder (AE) have the following drawbacks: (1) Establishing a memory bank requires additional memory space. (2)…
AutoAdversary: A Pixel Pruning Method for Sparse Adversarial Attack
Jinqiao Li, Xiaotao Liu, Jian Zhao +1
Deep neural networks (DNNs) have been proven to be vulnerable to adversarial examples. A special branch of adversarial examples, namely sparse adversarial examples, can fool the ta…
Robust and High Performance Face Detector
Yundong Zhang, Xiang Xu, Xiaotao Liu
In recent years, face detection has experienced significant performance improvement with the boost of deep convolutional neural networks. In this report, we reimplement the state-o…