230 citations · 262 across the 24 of their papers we have counts for
26 papers
CarryOn: A Dual-Loop Harness for Personalized and Reliable Robot Assistance
Zixuan Wang, Yuqi Liu, Yuxin Chen +7
While generalist vision-language-action (VLA) models have enabled diverse open-world manipulation, real-world robotic assistance requires two critical capabilities they currently l…
Beyond Data Scaling: Representation-Centric Continued Pre-training for Vision-Language-Action Models
Senqiao Yang, Chengyao Wang, Yuxin Chen +13
Scaling robot data is crucial for building generalist Vision-Language-Action (VLA) models, yet robot trajectories are harder to scale than web-scale image-text data because embodie…
XPolicyLab: A Unified Standard and Open Ecosystem for Robot Policy Evaluation and Deployment
XPolicyLab Community, Tianxing Chen, Yue Chen +67
Robot policy evaluation and deployment remain fragmented by model-specific software dependencies, data representations, and runtime interfaces, so that connecting N policies to M e…
StarVLA-: Reducing Complexity in Vision-Language-Action Systems
Jinhui Ye, Ning Gao, Senqiao Yang +7
Vision-Language-Action (VLA) models have recently emerged as a promising paradigm for building general-purpose robotic agents. However, the VLA landscape remains highly fragmented…
VP-VLA: Visual Prompting as an Interface for Vision-Language-Action Models
Zixuan Wang, Yuxin Chen, Yuqi Liu +6
Vision-Language-Action (VLA) models typically map visual observations and linguistic instructions directly to control signals. This "black-box" mapping forces a single forward pass…
Generative Distribution Distillation
Jiequan Cui, Beier Zhu, Qingshan Xu +6
In this paper, we formulate the knowledge distillation (KD) as a conditional generative problem and propose the \textit{Generative Distribution Distillation (GenDD)} framework. A n…
MTMamba++: Enhancing Multi-Task Dense Scene Understanding via Mamba-Based Decoders
Baijiong Lin, Weisen Jiang, Pengguang Chen +2
Multi-task dense scene understanding, which trains a model for multiple dense prediction tasks, has a wide range of application scenarios. Capturing long-range dependency and enhan…
MTMamba: Enhancing Multi-Task Dense Scene Understanding by Mamba-Based Decoders
Baijiong Lin, Weisen Jiang, Pengguang Chen +3
Multi-task dense scene understanding, which learns a model for multiple dense prediction tasks, has a wide range of application scenarios. Modeling long-range dependency and enhanc…
RoboCoder: Robotic Learning from Basic Skills to General Tasks with Large Language Models
Jingyao Li, Pengguang Chen, Sitong Wu +3
The emergence of Large Language Models (LLMs) has improved the prospects for robotic tasks. However, existing benchmarks are still limited to single tasks with limited generalizati…
MR-Ben: A Meta-Reasoning Benchmark for Evaluating System-2 Thinking in LLMs
Zhongshen Zeng, Yinhong Liu, Yingjia Wan +16
Large language models (LLMs) have shown increasing capability in problem-solving and decision-making, largely based on the step-by-step chain-of-thought reasoning processes. Howeve…
VLPose: Bridging the Domain Gap in Pose Estimation with Language-Vision Tuning
Jingyao Li, Pengguang Chen, Xuan Ju +2
Thanks to advances in deep learning techniques, Human Pose Estimation (HPE) has achieved significant progress in natural scenarios. However, these models perform poorly in artifici…
MOODv2: Masked Image Modeling for Out-of-Distribution Detection
Jingyao Li, Pengguang Chen, Shaozuo Yu +2
The crux of effective out-of-distribution (OOD) detection lies in acquiring a robust in-distribution (ID) representation, distinct from OOD samples. While previous methods predomin…
BAL: Balancing Diversity and Novelty for Active Learning
Jingyao Li, Pengguang Chen, Shaozuo Yu +2
The objective of Active Learning is to strategically label a subset of the dataset to maximize performance within a predetermined labeling budget. In this study, we harness feature…
MR-GSM8K: A Meta-Reasoning Benchmark for Large Language Model Evaluation
Zhongshen Zeng, Pengguang Chen, Shu Liu +2
In this work, we introduce a novel evaluation paradigm for Large Language Models (LLMs) that compels them to transition from a traditional question-answering role, akin to a studen…
MoTCoder: Elevating Large Language Models with Modular of Thought for Challenging Programming Tasks
Jingyao Li, Pengguang Chen, Bin Xia +2
Large Language Models (LLMs) have showcased impressive capabilities in handling straightforward programming tasks. However, their performance tends to falter when confronted with m…
Defect Spectrum: A Granular Look of Large-Scale Defect Datasets with Rich Semantics
Shuai Yang, Zhifei Chen, Pengguang Chen +4
Defect inspection is paramount within the closed-loop manufacturing system. However, existing datasets for defect inspection often lack precision and semantic granularity required…
Dual-Balancing for Multi-Task Learning
Baijiong Lin, Weisen Jiang, Feiyang Ye +6
Multi-task learning aims to learn multiple related tasks simultaneously and has achieved great success in various fields. However, the disparity in loss and gradient scales among t…
TagCLIP: Improving Discrimination Ability of Open-Vocabulary Semantic Segmentation
Jingyao Li, Pengguang Chen, Shengju Qian +2
Contrastive Language-Image Pre-training (CLIP) has recently shown great promise in pixel-level zero-shot learning tasks. However, existing approaches utilizing CLIP's text and patc…
Rethinking Out-of-distribution (OOD) Detection: Masked Image Modeling is All You Need
Jingyao Li, Pengguang Chen, Shaozuo Yu +3
The core of out-of-distribution (OOD) detection is to learn the in-distribution (ID) representation, which is distinguishable from OOD samples. Previous work applied recognition-ba…
SEA: Bridging the Gap Between One- and Two-stage Detector Distillation via SEmantic-aware Alignment
Yixin Chen, Zhuotao Tian, Pengguang Chen +2
We revisit the one- and two-stage detector distillation tasks and present a simple and efficient semantic-aware framework to fill the gap between them. We address the pixel-level i…
Adversarial Attacks on ML Defense Models Competition
Yinpeng Dong, Qi-An Fu, Xiao Yang +25
Due to the vulnerability of deep neural networks (DNNs) to adversarial examples, a large number of defense techniques have been proposed to alleviate this problem in recent years.…
Deep Structured Instance Graph for Distilling Object Detectors
Yixin Chen, Pengguang Chen, Shu Liu +2
Effectively structuring deep knowledge plays a pivotal role in transfer from teacher to student, especially in semantic vision tasks. In this paper, we present a simple knowledge s…
Exploring and Improving Mobile Level Vision Transformers
Pengguang Chen, Yixin Chen, Shu Liu +2
We study the vision transformer structure in the mobile level in this paper, and find a dramatic performance drop. We analyze the reason behind this phenomenon, and propose a novel…
Distilling Knowledge via Knowledge Review
Pengguang Chen, Shu Liu, Hengshuang Zhao +1
Knowledge distillation transfers knowledge from the teacher network to the student one, with the goal of greatly improving the performance of the student network. Previous methods…