4 citations · 8 across the 17 of their papers we have counts for
20 papers
MaskSense: Confronting the Visual Exploration Trap in Masked Image Generation
Yawen Shao, Jie Xiao, Kai Zhu +6
Reinforcement learning (RL) has shown strong potential for aligning generative models with human intent, but adapting RL to masked generative models (MGMs) remains largely underexp…
Back on Track: Aligning Rewards and States for Reasoning in Diffusion Large Language Models
Yawen Shao, Jie Xiao, Kai Zhu +6
Reinforcement learning (RL) holds immense promise for enhancing the reasoning capabilities of diffusion large language models (dLLMs). However, progress is fundamentally constraine…
IIB-LPO: Latent Policy Optimization via Iterative Information Bottleneck
Huilin Deng, Hongchen Luo, Yue Zhu +8
Recent advances in Reinforcement Learning with Verifiable Rewards (RLVR) for Large Language Model (LLM) reasoning have been hindered by a persistent challenge: exploration collapse…
Boosting the Generalization and Reasoning of Vision Language Models with Curriculum Reinforcement Learning
Huilin Deng, Ding Zou, Rui Ma +3
While state-of-the-art vision-language models (VLMs) have demonstrated remarkable capabilities in complex visual-text tasks, their success heavily relies on massive model scaling,…
Leverage Task Context for Object Affordance Ranking
Haojie Huang, Hongchen Luo, Wei Zhai +2
Intelligent agents accomplish different tasks by utilizing various objects based on their affordance, but how to select appropriate objects according to task context is not well-ex…
GREAT: Geometry-Intention Collaborative Inference for Open-Vocabulary 3D Object Affordance Grounding
Yawen Shao, Wei Zhai, Yuhang Yang +3
Open-Vocabulary 3D object affordance grounding aims to anticipate ``action possibilities'' regions on 3D objects with arbitrary instructions, which is crucial for robots to generic…
Visual-Geometric Collaborative Guidance for Affordance Learning
Hongchen Luo, Wei Zhai, Jiao Wang +2
Perceiving potential ``action possibilities'' (\ie, affordance) regions of images and learning interactive functionalities of objects from human demonstration is a challenging task…
Grounding 3D Scene Affordance From Egocentric Interactions
Cuiyu Liu, Wei Zhai, Yuhang Yang +4
Grounding 3D scene affordance aims to locate interactive regions in 3D environments, which is crucial for embodied agents to interact intelligently with their surroundings. Most ex…
VMAD: Visual-enhanced Multimodal Large Language Model for Zero-Shot Anomaly Detection
Huilin Deng, Hongchen Luo, Wei Zhai +2
Zero-shot anomaly detection (ZSAD) recognizes and localizes anomalies in previously unseen objects by establishing feature mapping between textual prompts and inspection images, de…
PEAR: Phrase-Based Hand-Object Interaction Anticipation
Zichen Zhang, Hongchen Luo, Wei Zhai +2
First-person hand-object interaction anticipation aims to predict the interaction process over a forthcoming period based on current scenes and prompts. This capability is crucial…
Bidirectional Progressive Transformer for Interaction Intention Anticipation
Zichen Zhang, Hongchen Luo, Wei Zhai +2
Interaction intention anticipation aims to jointly predict future hand trajectories and interaction hotspots. Existing research often treated trajectory forecasting and interaction…
Intention-driven Ego-to-Exo Video Generation
Hongchen Luo, Kai Zhu, Wei Zhai +1
Ego-to-exo video generation refers to generating the corresponding exocentric video according to the egocentric video, providing valuable applications in AR/VR and embodied AI. Ben…
LEMON: Learning 3D Human-Object Interaction Relation from 2D Images
Yuhang Yang, Wei Zhai, Hongchen Luo +2
Learning 3D human-object interaction relation is pivotal to embodied AI and interaction modeling. Most existing methods approach the goal by learning to predict isolated interactio…
Grounding 3D Object Affordance from 2D Interactions in Images
Yuhang Yang, Wei Zhai, Hongchen Luo +3
Grounding 3D object affordance seeks to locate objects' ''action possibilities'' regions in the 3D space, which serves as a link between perception and operation for embodied agent…
Grounded Affordance from Exocentric View
Hongchen Luo, Wei Zhai, Jing Zhang +2
Affordance grounding aims to locate objects' "action possibilities" regions, which is an essential step toward embodied intelligence. Due to the diversity of interactive affordance…
Learning Affordance Grounding from Exocentric Images
Hongchen Luo, Wei Zhai, Jing Zhang +2
Affordance grounding, a task to ground (i.e., localize) action possibility region in objects, which faces the challenge of establishing an explicit link with object parts due to th…
Phrase-Based Affordance Detection via Cyclic Bilateral Interaction
Liangsheng Lu, Wei Zhai, Hongchen Luo +2
Affordance detection, which refers to perceiving objects with potential action possibilities in images, is a challenging task since the possible affordance depends on the person's…
Learning Visual Affordance Grounding from Demonstration Videos
Hongchen Luo, Wei Zhai, Jing Zhang +2
Visual affordance grounding aims to segment all possible interaction regions between people and objects from an image/video, which is beneficial for many applications, such as robo…
One-Shot Object Affordance Detection in the Wild
Wei Zhai, Hongchen Luo, Jing Zhang +2
Affordance detection refers to identifying the potential action possibilities of objects in an image, which is a crucial ability for robot perception and manipulation. To empower r…
One-Shot Affordance Detection
Hongchen Luo, Wei Zhai, Jing Zhang +2
Affordance detection refers to identifying the potential action possibilities of objects in an image, which is an important ability for robot perception and manipulation. To empowe…