8 citations · 20 across the 12 of their papers we have counts for
12 papers
Emulating Human-like Adaptive Vision for Efficient and Flexible Machine Visual Perception
Yulin Wang, Yang Yue, Huanqian Wang +11
Human vision is highly adaptive, efficiently sampling intricate environments by sequentially fixating on task-relevant regions. In contrast, prevailing machine vision models passiv…
PIG-Nav: Key Insights for Pretrained Image Goal Navigation Models
Jiansong Wan, Chengming Zhou, Jinkua Liu +14
Recent studies have explored pretrained (foundation) models for vision-based robotic navigation, aiming to achieve generalizable navigation and positive transfer across diverse env…
PsychoGAT: A Novel Psychological Measurement Paradigm through Interactive Fiction Games with LLM Agents
Qisen Yang, Zekun Wang, Honghui Chen +6
Psychological measurement is essential for mental health, self-understanding, and personal development. Traditional methods, such as self-report scales and psychologist interviews,…
Train Once, Get a Family: State-Adaptive Balances for Offline-to-Online Reinforcement Learning
Shenzhi Wang, Qisen Yang, Jiawei Gao +6
Offline-to-online reinforcement learning (RL) is a training paradigm that combines pre-training on a pre-collected dataset with fine-tuning in an online environment. However, the i…
Avalon's Game of Thoughts: Battle Against Deception through Recursive Contemplation
Shenzhi Wang, Chang Liu, Zilong Zheng +7
Recent breakthroughs in large language models (LLMs) have brought remarkable success in the field of LLM-as-Agent. Nevertheless, a prevalent assumption is that the information proc…
Leveraging Reward Consistency for Interpretable Feature Discovery in Reinforcement Learning
Qisen Yang, Huanqian Wang, Mukun Tong +3
The black-box nature of deep reinforcement learning (RL) hinders them from real-world applications. Therefore, interpreting and explaining RL agents have been active research topic…
Hundreds Guide Millions: Adaptive Offline Reinforcement Learning with Expert Guidance
Qisen Yang, Shenzhi Wang, Qihang Zhang +2
Offline reinforcement learning (RL) optimizes the policy on a previously collected dataset without any interactions with the environment, yet usually suffers from the distributiona…
Boosting Offline Reinforcement Learning with Action Preference Query
Qisen Yang, Shenzhi Wang, Matthieu Gaetan Lin +2
Training practical agents usually involve offline and online reinforcement learning (RL) to balance the policy's performance and interaction costs. In particular, online fine-tunin…
Decoupled Prioritized Resampling for Offline RL
Yang Yue, Bingyi Kang, Xiao Ma +4
Offline reinforcement learning (RL) is challenged by the distributional shift problem. To address this problem, existing works mainly focus on designing sophisticated policy constr…
Efficient Knowledge Distillation from Model Checkpoints
Chaofei Wang, Qisen Yang, Rui Huang +2
Knowledge distillation is an effective approach to learn compact models (students) with the supervision of large and strong models (teachers). As empirically there exists a strong…
Fine-Grained Few Shot Learning with Foreground Object Transformation
Chaofei Wang, Shiji Song, Qisen Yang +2
Traditional fine-grained image classification generally requires abundant labeled samples to deal with the low inter-class variance but high intra-class variance problem. However,…
CAM-loss: Towards Learning Spatially Discriminative Feature Representations
Chaofei Wang, Jiayu Xiao, Yizeng Han +3
The backbone of traditional CNN classifier is generally considered as a feature extractor, followed by a linear layer which performs the classification. We propose a novel loss fun…