1 code implementation • 30 Apr 2024 • Zhanwei Zhang, Zishuo Hua, Minghao Chen, Wei Lu, Binbin Lin, Deng Cai, Wenxiao Wang
Finally, to ensure the optimal granularity of key steps, we design a selectable granularity strategy that caters to each predicted trajectory.
no code implementations • 30 Apr 2024 • Zhanwei Zhang, Minghao Chen, Shuai Xiao, Liang Peng, Hengjia Li, Binbin Lin, Ping Li, Wenxiao Wang, Boxi Wu, Deng Cai
Specifically, in the selection process, to improve the reliability of pseudo boxes, we propose a complementary augmentation strategy.
1 code implementation • 22 Feb 2024 • Chenxi Huang, Yuenan Hou, Weicai Ye, Di Huang, Xiaoshui Huang, Binbin Lin, Deng Cai, Wanli Ouyang
We project the freely available 3D segmentation annotations onto the 2D plane and leverage the corresponding 2D semantic maps as the supervision signal, significantly enhancing the semantic awareness of multi-view detectors.
no code implementations • 15 Feb 2024 • Wenxiao Wang, Wei Chen, Yicong Luo, Yongliu Long, Zhengkai Lin, Liye Zhang, Binbin Lin, Deng Cai, Xiaofei He
However, Large language models have two prominent characteristics compared to smaller models: (1) Most of compression algorithms require finetuning or even retraining the model after compression.
1 code implementation • 20 Dec 2023 • Yuqi Lin, Minghao Chen, Kaipeng Zhang, Hengjia Li, Mingming Li, Zheng Yang, Dongqin Lv, Binbin Lin, Haifeng Liu, Deng Cai
As a result, we dissect the preservation of patch-wise spatial information in CLIP and proposed a local-to-global framework to obtain image tags.
1 code implementation • 12 Oct 2023 • Honghui Yang, Sha Zhang, Di Huang, Xiaoyang Wu, Haoyi Zhu, Tong He, Shixiang Tang, Hengshuang Zhao, Qibo Qiu, Binbin Lin, Xiaofei He, Wanli Ouyang
In the context of autonomous driving, the significance of effective feature learning is widely acknowledged.
no code implementations • 21 Sep 2023 • Ping Li, Yu Zhang, Li Yuan, Huaxin Xiao, Binbin Lin, Xianghua Xu
Unsupervised Video Object Segmentation (VOS) aims at identifying the contours of primary foreground objects in videos without any prior knowledge.
Semantic Segmentation Unsupervised Video Object Segmentation +1
1 code implementation • 1 Aug 2023 • Zhihao Chi, Tu Zheng, Hengjia Li, Zheng Yang, Boxi Wu, Binbin Lin, Deng Cai
In this paper, we restudy the hyper-parameter temperature and figure out its incapability to distill the knowledge from each sample sufficiently when it is a single value.
no code implementations • 1 Aug 2023 • Minghao Chen, Zepeng Gao, Shuai Zhao, Qibo Qiu, Wenxiao Wang, Binbin Lin, Xiaofei He
Unsupervised domain adaptation (UDA) methods facilitate the transfer of models to target domains without labels.
no code implementations • 17 Jun 2023 • Ping Li, Junjie Chen, Binbin Lin, Xianghua Xu
Specifically, we employ an asymmetric encoder to learn the compensating features of the RGB and the thermal images.
Ranked #17 on Thermal Image Segmentation on MFN Dataset
1 code implementation • CVPR 2023 • Honghui Yang, Wenxiao Wang, Minghao Chen, Binbin Lin, Tong He, Hua Chen, Xiaofei He, Wanli Ouyang
The key to associating the two different representations is our introduced input-dependent Query Initialization module, which could efficiently generate reference points and content queries.
no code implementations • 31 Mar 2023 • Hengjia Li, Tu Zheng, Zhihao Chi, Zheng Yang, Wenxiao Wang, Boxi Wu, Binbin Lin, Deng Cai
To tackle these problems, we propose Asymmetric Parallel Point Transformer (APPT).
no code implementations • 27 Mar 2023 • Chenxi Huang, Liang Xie, Yibo Yang, Wenxiao Wang, Binbin Lin, Deng Cai
One of the challenges in federated learning is the non-independent and identically distributed (non-iid) characteristics between heterogeneous devices, which cause significant differences in local updates and affect the performance of the central server.
1 code implementation • 13 Mar 2023 • Wenxiao Wang, Wei Chen, Qibo Qiu, Long Chen, Boxi Wu, Binbin Lin, Xiaofei He, Wei Liu
On the one hand, CEL blends each token with multiple patches of different scales, providing the self-attention module itself with cross-scale features.
no code implementations • 20 Feb 2023 • Liang Xie, Yibo Yang, Wenxiao Wang, Binbin Lin, Deng Cai, Xiaofei He, Ronghua Liang
Compared to 2D images, 3D point clouds are much more sensitive to rotations.
1 code implementation • 20 Dec 2022 • Chenxi Huang, Tong He, Haidong Ren, Wenxiao Wang, Binbin Lin, Deng Cai
Unfortunately, the network cannot accurately distinguish different depths from such non-discriminative visual features, resulting in unstable depth training.
1 code implementation • CVPR 2023 • Yuqi Lin, Minghao Chen, Wenxiao Wang, Boxi Wu, Ke Li, Binbin Lin, Haifeng Liu, Xiaofei He
To efficiently generate high-quality segmentation masks from CLIP, we propose a novel WSSS framework called CLIP-ES.
Ranked #12 on Weakly-Supervised Semantic Segmentation on COCO 2014 val
1 code implementation • CVPR 2023 • Honghui Yang, Tong He, Jiaheng Liu, Hua Chen, Boxi Wu, Binbin Lin, Xiaofei He, Wanli Ouyang
In contrast to previous 3D MAE frameworks, which either design a complex decoder to infer masked information from maintained regions or adopt sophisticated masking strategies, we instead propose a much simpler paradigm.
no code implementations • ICCV 2023 • Yangyi Huang, Hongwei Yi, Weiyang Liu, Haofan Wang, Boxi Wu, Wenxiao Wang, Binbin Lin, Debing Zhang, Deng Cai
Most of these methods fail to achieve realistic reconstruction when only a single image is available.
no code implementations • 14 Nov 2022 • Xiaopei Wu, Yang Zhao, Liang Peng, Hua Chen, Xiaoshui Huang, Binbin Lin, Haifeng Liu, Deng Cai, Wanli Ouyang
When training a teacher-student semi-supervised framework, we randomly select gt samples and pseudo samples to both labeled frames and unlabeled frames, making a strong data augmentation for them.
no code implementations • 29 Aug 2022 • Boxi Wu, Jie Jiang, Haidong Ren, Zifan Du, Wenxiao Wang, Zhifeng Li, Deng Cai, Xiaofei He, Binbin Lin, Wei Liu
Various training criteria for these auxiliary outliers are proposed based on heuristic intuitions.
1 code implementation • 22 Dec 2021 • Weigang Lu, Yibing Zhan, Binbin Lin, Ziyu Guan, Liu Liu, Baosheng Yu, Wei Zhao, Yaming Yang, DaCheng Tao
In this paper, we conduct theoretical and experimental analysis to explore the fundamental causes of performance degradation in deep GCNs: over-smoothing and gradient vanishing have a mutually reinforcing effect that causes the performance to deteriorate more quickly in deep GCNs.
3 code implementations • ICLR 2022 • Wenxiao Wang, Lu Yao, Long Chen, Binbin Lin, Deng Cai, Xiaofei He, Wei Liu
On the one hand, CEL blends each embedding with multiple patches of different scales, providing the self-attention module itself with cross-scale features.
Ranked #42 on Semantic Segmentation on ADE20K val
no code implementations • 23 Jun 2017 • Hemanth Venkateswara, Prasanth Lade, Binbin Lin, Jieping Ye, Sethuraman Panchanathan
Estimating the MI for a subset of features is often intractable.
no code implementations • 30 Jul 2014 • Binbin Lin, Qingyang Li, Qian Sun, Ming-Jun Lai, Ian Davidson, Wei Fan, Jieping Ye
The effectiveness of gene expression pattern annotation relies on the quality of feature representation.
no code implementations • 1 May 2014 • Binbin Lin, Ji Yang, Xiaofei He, Jieping Ye
Based on our theoretical analysis, we propose to first learn the gradient field of the distance function and then learn the distance function itself.
no code implementations • NeurIPS 2012 • Binbin Lin, Sen yang, Chiyuan Zhang, Jieping Ye, Xiaofei He
MTVFL has the following key properties: (1) the vector fields we learned are close to the gradient fields of the prediction functions; (2) within each task, the vector field is required to be as parallel as possible which is expected to span a low dimensional subspace; (3) the vector fields from all tasks share a low dimensional subspace.
no code implementations • 27 Jun 2012 • Ming Ji, Tianbao Yang, Binbin Lin, Rong Jin, Jiawei Han
In this work, we develop a simple algorithm for semi-supervised regression.
no code implementations • NeurIPS 2011 • Binbin Lin, Chiyuan Zhang, Xiaofei He
To achieve this goal, we show that the second order smoothness measures the linearity of the function, and the gradient field of a linear function has to be a parallel vector field.