7 citations · 17 across the 64 of their papers we have counts for
21 papers · 1 filter
Rethinking Long-tailed Dataset Distillation: A Uni-Level Framework with Unbiased Recovery and Relabeling
Xiao Cui, Yulei Qin, Xinyue Li +3
Dataset distillation creates a small distilled set that enables efficient training by capturing key information from the full dataset. While existing dataset distillation methods p…
Gait-Adaptive Perceptive Humanoid Locomotion with Real-Time Under-Base Terrain Reconstruction
Haolin Song, Hongbo Zhu, Tao Yu +5
For full-size humanoid robots, even with recent advances in reinforcement learning-based control, achieving reliable locomotion on complex terrains, such as long staircases, remain…
Make-It-Poseable: Feed-forward Latent Posing Model for 3D Characters
Zhiyang Guo, Ori Zhang, Jax Xiang +4
Posing 3D characters is a fundamental task in computer graphics. However, existing paradigms, ranging from traditional auto-rigging to recent pose-conditioned generative models, fr…
Optimizing Distributional Geometry Alignment with Optimal Transport for Generative Dataset Distillation
Xiao Cui, Yulei Qin, Wengang Zhou +2
Dataset distillation seeks to synthesize a compact distilled dataset, enabling models trained on it to achieve performance comparable to models trained on the full dataset. Recent…
Enhancing the Outcome Reward-based RL Training of MLLMs with Self-Consistency Sampling
Jiahao Wang, Weiye Xu, Aijun Yang +5
Outcome-reward reinforcement learning (RL) is a common and increasingly significant way to refine the step-by-step reasoning of multimodal large language models (MLLMs). In the mul…
ScaleWeaver: Weaving Efficient Controllable T2I Generation with Multi-Scale Reference Attention
Keli Liu, Zhendong Wang, Wengang Zhou +3
Text-to-image generation with visual autoregressive~(VAR) models has recently achieved impressive advances in generation fidelity and inference efficiency. While control mechanisms…
Goal Discovery with Causal Capacity for Efficient Reinforcement Learning
Yan Yu, Yaodong Yang, Zhengbo Lu +3
Causal inference is crucial for humans to explore the world, which can be modeled to enable an agent to efficiently explore the environment in reinforcement learning. Existing rese…
SLRTP2025 Sign Language Production Challenge: Methodology, Results, and Future Work
Harry Walsh, Ed Fish, Ozge Mercanoglu Sincan +19
Sign Language Production (SLP) is the task of generating sign language video from spoken language inputs. The field has seen a range of innovations over the last few years, with th…
Search-Based Credit Assignment for Offline Preference-Based Reinforcement Learning
Xiancheng Gao, Yufeng Shi, Wengang Zhou +1
Offline reinforcement learning refers to the process of learning policies from fixed datasets, without requiring additional environment interaction. However, it often relies on wel…
DocR1: Evidence Page-Guided GRPO for Multi-Page Document Understanding
Junyu Xiong, Yonghui Wang, Weichao Zhao +4
Understanding multi-page documents poses a significant challenge for multimodal large language models (MLLMs), as it requires fine-grained visual comprehension and multi-hop reason…
Robust Multimodal Large Language Models Against Modality Conflict
Zongmeng Zhang, Wengang Zhou, Jie Zhao +1
Despite the impressive capabilities of multimodal large language models (MLLMs) in vision-language tasks, they are prone to hallucinations in real-world scenarios. This paper inves…
Self-Classification Enhancement and Correction for Weakly Supervised Object Detection
Yufei Yin, Lechao Cheng, Wengang Zhou +3
In recent years, weakly supervised object detection (WSOD) has attracted much attention due to its low labeling cost. The success of recent WSOD models is often ascribed to the two…
VisuLogic: A Benchmark for Evaluating Visual Reasoning in Multi-modal Large Language Models
Weiye Xu, Jiahao Wang, Weiyun Wang +10
Visual reasoning is a core component of human intelligence and a critical capability for advanced multimodal models. Yet current reasoning evaluations of multimodal large language…
Cross-Modal Consistency Learning for Sign Language Recognition
Kepeng Wu, Zecheng Li, Hezhen Hu +2
Pre-training has been proven to be effective in boosting the performance of Isolated Sign Language Recognition (ISLR). Existing pre-training methods solely focus on the compact pos…
S3R-GS: Streamlining the Pipeline for Large-Scale Street Scene Reconstruction
Guangting Zheng, Jiajun Deng, Xiaomeng Chu +3
Recently, 3D Gaussian Splatting (3DGS) has reshaped the field of photorealistic 3D reconstruction, achieving impressive rendering quality and speed. However, when applied to large-…
Multi-Cue Adaptive Visual Token Pruning for Large Vision-Language Models
Bozhi Luan, Wengang Zhou, Hao Feng +3
As the computational needs of Large Vision-Language Models (LVLMs) increase, visual token pruning has proven effective in improving inference speed and memory efficiency. Tradition…
DesignDiffusion: High-Quality Text-to-Design Image Generation with Diffusion Models
Zhendong Wang, Jianmin Bao, Shuyang Gu +3
In this paper, we present DesignDiffusion, a simple yet effective framework for the novel task of synthesizing design images from textual descriptions. A primary challenge lies in…
Model Evolution Framework with Genetic Algorithm for Multi-Task Reinforcement Learning
Yan Yu, Wengang Zhou, Yaodong Yang +3
Multi-task reinforcement learning employs a single policy to complete various tasks, aiming to develop an agent with generalizability across different scenarios. Given the shared c…
Multi-Level Optimal Transport for Universal Cross-Tokenizer Knowledge Distillation on Language Models
Xiao Cui, Mo Zhu, Yulei Qin +3
Knowledge distillation (KD) has become a prevalent technique for compressing large language models (LLMs). Existing KD methods are constrained by the need for identical tokenizers…
Uni-Sign: Toward Unified Sign Language Understanding at Scale
Zecheng Li, Wengang Zhou, Weichao Zhao +3
Sign language pre-training has gained increasing attention for its ability to enhance performance across various sign language understanding (SLU) tasks. However, existing methods…
SmartEraser: Remove Anything from Images using Masked-Region Guidance
Longtao Jiang, Zhendong Wang, Jianmin Bao +5
Object removal has so far been dominated by the mask-and-inpaint paradigm, where the masked region is excluded from the input, leaving models relying on unmasked areas to inpaint t…