21 citations · 48 across the 14 of their papers we have counts for
14 papers
EATR-Stereo: Embodiment-Aware Token Routing of Paired Stereo Evidence for Humanoid Vision-Language-Action Control
Songwei Wu, Rui Zhao, Fan Yang +7
Long-horizon humanoid vision--language--action (VLA) control with head-mounted stereo cameras requires visual interfaces that can exploit complementary views while maintaining comp…
Balancing Logit Variation for Long-tailed Semantic Segmentation
Yuchao Wang, Jingjing Fei, Haochen Wang +5
Semantic segmentation usually suffers from a long-tail data distribution. Due to the imbalanced number of samples across categories, the features of those tail classes may get sque…
ZeroPose: CAD-Prompted Zero-shot Object 6D Pose Estimation in Cluttered Scenes
Jianqiu Chen, Zikun Zhou, Mingshan Sun +4
Many robotics and industry applications have a high demand for the capability to estimate the 6D pose of novel objects from the cluttered scene. However, existing classic pose esti…
SeqCo-DETR: Sequence Consistency Training for Self-Supervised Object Detection with Transformers
Guoqiang Jin, Fan Yang, Mingshan Sun +7
Self-supervised pre-training and transformer-based networks have significantly improved the performance of object detection. However, most of the current self-supervised object det…
Efficient Masked Autoencoders with Self-Consistency
Zhaowen Li, Yousong Zhu, Zhiyang Chen +5
Inspired by the masked language modeling (MLM) in natural language processing tasks, the masked image modeling (MIM) has been recognized as a strong self-supervised pre-training me…
Exploring Stochastic Autoregressive Image Modeling for Visual Representation
Yu Qi, Fan Yang, Yousong Zhu +4
Autoregressive language modeling (ALM) have been successfully used in self-supervised pre-training in Natural language processing (NLP). However, this paradigm has not achieved com…
MIAD: A Maintenance Inspection Dataset for Unsupervised Anomaly Detection
Tianpeng Bao, Jiadong Chen, Wei Li +5
Visual anomaly detection plays a crucial role in not only manufacturing inspection to find defects of products during manufacturing processes, but also maintenance inspection to ke…
Geo6D: Geometric Constraints Learning for 6D Pose Estimation
Jianqiu Chen, Mingshan Sun, Ye Zheng +7
Numerous 6D pose estimation methods have been proposed that employ end-to-end regression to directly estimate the target pose parameters. Since the visible features of objects are…
Obj2Seq: Formatting Objects as Sequences with Class Prompt for Visual Tasks
Zhiyang Chen, Yousong Zhu, Zhaowen Li +8
Visual tasks vary a lot in their output formats and concerned contents, therefore it is hard to process them with an identical structure. One main obstacle lies in the high-dimensi…
Learning from Future: A Novel Self-Training Framework for Semantic Segmentation
Ye Du, Yujun Shen, Haochen Wang +6
Self-training has shown great potential in semi-supervised learning. Its core idea is to use the model learned on labeled data to generate pseudo-labels for unlabeled samples, and…
Uni6Dv2: Noise Elimination for 6D Pose Estimation
Mingshan Sun, Ye Zheng, Tianpeng Bao +5
Uni6D is the first 6D pose estimation approach to employ a unified backbone network to extract features from both RGB and depth images. We discover that the principal reasons of Un…
Uni6D: A Unified CNN Framework without Projection Breakdown for 6D Pose Estimation
Xiaoke Jiang, Donghai Li, Hao Chen +3
As RGB-D sensors become more affordable, using RGB-D images to obtain high-accuracy 6D pose estimation results becomes a better option. State-of-the-art approaches typically use di…
UniVIP: A Unified Framework for Self-Supervised Visual Pre-training
Zhaowen Li, Yousong Zhu, Fan Yang +9
Self-supervised learning (SSL) holds promise in leveraging large amounts of unlabeled data. However, the success of popular SSL methods has limited on single-centric-object images…
Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels
Yuchao Wang, Haochen Wang, Yujun Shen +6
The crux of semi-supervised semantic segmentation is to assign adequate pseudo-labels to the pixels of unlabeled images. A common practice is to select the highly confident predict…