no code implementations • ECCV 2020 • Haochen Wang, Xu-Dong Zhang, Yutao Hu, Yandan Yang, Xian-Bin Cao, Xian-Tong Zhen
The crux of few-shot segmentation is to extract object information from the support image and then propagate it to guide the segmentation of query images.
no code implementations • 2 Apr 2024 • Joshua Ahn, Haochen Wang, Raymond A. Yeh, Greg Shakhnarovich
Scale-ambiguity in 3D scene dimensions leads to magnitude-ambiguity of volumetric densities in neural radiance fields, i. e., the densities double when scene size is halved, and vice versa.
3 code implementations • 18 Mar 2024 • Hongbo Zhao, Bolin Ni, Haochen Wang, Junsong Fan, Fei Zhu, Yuxi Wang, Yuntao Chen, Gaofeng Meng, Zhaoxiang Zhang
(i) For unwanted knowledge, efficient and effective deleting is crucial.
no code implementations • 29 Jan 2024 • Jie Liu, Wenzhe Yin, Haochen Wang, Yunlu Chen, Jan-Jakob Sonke, Efstratios Gavves
Existing prototype-based methods rely on support prototypes to guide the segmentation of query point clouds, but they encounter challenges when significant object variations exist between the support prototypes and query features.
1 code implementation • 21 Dec 2023 • Haochen Wang, Junsong Fan, Yuxi Wang, Kaiyou Song, Tiancai Wang, Xiangyu Zhang, Zhaoxiang Zhang
To empower the model as a teacher, we propose Hard Patches Mining (HPM), predicting patch-wise losses and subsequently determining where to mask.
1 code implementation • NeurIPS 2023 • Haochen Wang, Junsong Fan, Yuxi Wang, Kaiyou Song, Tong Wang, Zhaoxiang Zhang
As it is empirically observed that Vision Transformers (ViTs) are quite insensitive to the order of input tokens, the need for an appropriate self-supervised pretext task that enhances the location awareness of ViTs is becoming evident.
no code implementations • 4 Jun 2023 • Haochen Wang, Yuchao Wang, Yujun Shen, Junsong Fan, Yuxi Wang, Zhaoxiang Zhang
A common practice is to select the highly confident predictions as the pseudo-ground-truths for each pixel, but it leads to a problem that most pixels may be left unused due to their unreliability.
1 code implementation • CVPR 2023 • Yuchao Wang, Jingjing Fei, Haochen Wang, Wei Li, Tianpeng Bao, Liwei Wu, Rui Zhao, Yujun Shen
In this way, we manage to close the gap between the feature areas of different categories, resulting in a more balanced representation.
no code implementations • 23 May 2023 • Haochen Wang, Yujun Shen, Jingjing Fei, Wei Li, Liwei Wu, Yuxi Wang, Zhaoxiang Zhang
To this end, we propose T2S-DA, which we interpret as a form of pulling Target to Source for Domain Adaptation, encouraging the model in learning similar cross-domain features.
1 code implementation • 23 Apr 2023 • Cilin Yan, Haochen Wang, Jie Liu, XiaoLong Jiang, Yao Hu, Xu Tang, Guoliang Kang, Efstratios Gavves
Click-based interactive segmentation aims to generate target masks via human clicking, which facilitates efficient pixel-level annotation and image editing.
1 code implementation • CVPR 2023 • Haochen Wang, Kaiyou Song, Junsong Fan, Yuxi Wang, Jin Xie, Zhaoxiang Zhang
We observe that the reconstruction loss can naturally be the metric of the difficulty of the pre-training task.
1 code implementation • ICCV 2023 • Haochen Wang, Cilin Yan, Shuai Wang, XiaoLong Jiang, Xu Tang, Yao Hu, Weidi Xie, Efstratios Gavves
Video Instance Segmentation (VIS) aims at segmenting and categorizing objects in videos from a closed set of training categories, lacking the generalization ability to handle novel categories in real-world videos.
no code implementations • 9 Jan 2023 • Jie Liu, Yanqi Bao, Wenzhe Yin, Haochen Wang, Yang Gao, Jan-Jakob Sonke, Efstratios Gavves
However, the appearance variations between objects from the same category could be extremely large, leading to unreliable feature matching and query mask prediction.
Ranked #40 on Few-Shot Semantic Segmentation on PASCAL-5i (1-Shot)
no code implementations • 9 Dec 2022 • Nam Anh Dinh, Haochen Wang, Greg Shakhnarovich, Rana Hanocka
There is no settled universal 3D representation for geometry with many alternatives such as point clouds, meshes, implicit functions, and voxels to name a few.
1 code implementation • CVPR 2023 • Haochen Wang, Xiaodan Du, Jiahao Li, Raymond A. Yeh, Greg Shakhnarovich
We propose to apply chain rule on the learned gradients, and back-propagate the score of a diffusion model through the Jacobian of a differentiable renderer, which we instantiate to be a voxel radiance field.
Ranked #6 on Text to 3D on T$^3$Bench
1 code implementation • 15 Sep 2022 • Ye Du, Yujun Shen, Haochen Wang, Jingjing Fei, Wei Li, Liwei Wu, Rui Zhao, Zehua Fu, Qingjie Liu
Self-training has shown great potential in semi-supervised learning.
1 code implementation • CVPR 2022 • Haochen Wang, Jiayi Shen, Yongtuo Liu, Yan Gao, Efstratios Gavves
To tackle this issue, we propose a Neighbor Transformer Network, or NFormer, which explicitly models interactions across all input images, thus suppressing outlier features and leading to more robust representations overall.
1 code implementation • CVPR 2022 • Yuchao Wang, Haochen Wang, Yujun Shen, Jingjing Fei, Wei Li, Guoqiang Jin, Liwei Wu, Rui Zhao, Xinyi Le
A common practice is to select the highly confident predictions as the pseudo ground-truth, but it leads to a problem that most pixels may be left unused due to their unreliability.
no code implementations • 2 Feb 2022 • Yan Gao, Qimeng Wang, Xu Tang, Haochen Wang, Fei Ding, Jing Li, Yao Hu
Prior works propose to predict Intersection-over-Union (IoU) between bounding boxes and corresponding ground-truths to improve NMS, while accurately predicting IoU is still a challenging problem.
1 code implementation • 14 May 2021 • Haoliang Sun, Xiankai Lu, Haochen Wang, Yilong Yin, XianTong Zhen, Cees G. M. Snoek, Ling Shao
We define a global latent variable to represent the prototype of each object category, which we model as a probabilistic distribution.
1 code implementation • CVPR 2021 • Haochen Wang, XiaoLong Jiang, Haibing Ren, Yao Hu, Song Bai
In this work we present SwiftNet for real-time semisupervised video object segmentation (one-shot VOS), which reports 77. 8% J &F and 70 FPS on DAVIS 2017 validation dataset, leading all present solutions in overall accuracy and speed performance.
no code implementations • CVPR 2020 • Haochen Wang, Ruotian Luo, Michael Maire, Greg Shakhnarovich
The core of our approach, Pixel Consensus Voting, is a framework for instance segmentation based on the Generalized Hough transform.
Ranked #36 on Panoptic Segmentation on COCO test-dev
2 code implementations • 1 Aug 2019 • Igor Vasiljevic, Nick Kolkin, Shanyi Zhang, Ruotian Luo, Haochen Wang, Falcon Z. Dai, Andrea F. Daniele, Mohammadreza Mostajabi, Steven Basart, Matthew R. Walter, Gregory Shakhnarovich
We introduce DIODE, a dataset that contains thousands of diverse high resolution color images with accurate, dense, long-range depth measurements.