Search Results - grouping and shape analysis; Recognition: detection

Sparse Instance Activation for Real-Time Instance Segmentation by Cheng, Tianheng, Wang, Xinggang, Chen, Shaoyu, Zhang, Wenqiang, Zhang, Qian, Huang, Chang, Zhang, Zhaoxiang, Liu, Wenyu

“… Previously, most instance segmentation methods heavily rely on object detection and perform mask prediction based on bounding boxes or dense centers…”
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E2EC: An End-to-End Contour-based Method for High-Quality High-Speed Instance Segmentation by Zhang, Tao, Wei, Shiqing, Ji, Shunping

“…Contour-based instance segmentation methods have developed rapidly recently but feature rough and hand-crafted front-end contour initialization, which…”
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Open-World Instance Segmentation: Exploiting Pseudo Ground Truth From Learned Pairwise Affinity by Wang, Weiyao, Feiszli, Matt, Wang, Heng, Malik, Jitendra, Tran, Du

“…Open-world instance segmentation is the task of grouping pixels into object instances without any pre-determined taxonomy…”
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Unsupervised Hierarchical Semantic Segmentation with Multiview Cosegmentation and Clustering Transformers by Ke, Tsung-Wei, Hwang, Jyh-Jing, Guo, Yunhui, Wang, Xudong, Yu, Stella X.

“…Unsupervised semantic segmentation aims to discover groupings within and across images that capture object-and view-invariance of a category without external supervision…”
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CDGNet: Class Distribution Guided Network for Human Parsing by Liu, Kunliang, Choi, Ouk, Wang, Jianming, Hwang, Wonjun

“…The objective of human parsing is to partition a human in an image into constituent parts. This task involves labeling each pixel of the human image according…”
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GAT-CADNet: Graph Attention Network for Panoptic Symbol Spotting in CAD Drawings by Zheng, Zhaohua, Li, Jianfang, Zhu, Lingjie, Li, Honghua, Petzold, Frank, Tan, Ping

“… Our key contributions are three-fold: 1) the instance symbol spotting task is formulated as a subgraph detection problem and solved by predicting the adjacency matrix; 2…”
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Medial Spectral Coordinates for 3D Shape Analysis by Rezanejad, Morteza, Khodadad, Mohammad, Mahyar, Hamidreza, Lombaert, Herve, Gruninger, Michael, Walther, Dirk, Siddiqi, Kaleem

“…In recent years there has been a resurgence of interest in our community in the shape analysis of 3D objects repre-sented by surface meshes, their voxelized interiors, or surface point clouds…”
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Neural Recognition of Dashed Curves with Gestalt Law of Continuity by Liu, Hanyuan, Li, Chengze, Liu, Xueting, Wong, Tien-Tsin

“…Dashed curve is a frequently used curve form and is widely used in various drawing and illustration applications. While humans can intuitively recognize dashed…”
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Masked-attention Mask Transformer for Universal Image Segmentation by Cheng, Bowen, Misra, Ishan, Schwing, Alexander G., Kirillov, Alexander, Girdhar, Rohit

“… (50.1 AP on COCO) and semantic segmentation (57.7 mIoU onADE20K)…”
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PatchFormer: An Efficient Point Transformer with Patch Attention by Zhang, Cheng, Wan, Haocheng, Shen, Xinyi, Wu, Zizhao

“…) to adaptively learn a much smaller set of bases upon which the attention maps are computed. By a weighted summation upon these bases, PAT not only captures the global shape context but also achieves linear complexity to input size…”
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Eigencontours: Novel Contour Descriptors Based on Low-Rank Approximation by Park, Wonhui, Jin, Dongkwon, Kim, Chang-Su

“…Novel contour descriptors, called eigencontours, based on low-rank approximation are proposed in this paper. First, we construct a contour matrix containing…”
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SeeThroughNet: Resurrection of Auxiliary Loss by Preserving Class Probability Information by Han, Dasol, Yoo, Jaewook, Oh, Dokwan

“…Auxiliary loss is additional loss besides the main branch loss to help optimize the learning process of neural networks. In order to calculate the auxiliary…”
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RBGNet: Ray-based Grouping for 3D Object Detection by Wang, Haiyang, Shi, Shaoshuai, Yang, Ze, Fang, Rongyao, Qian, Qi, Li, Hongsheng, Schiele, Bernt, Wang, Liwei

“… In order to learn better representations of object shape to enhance cluster features for predicting 3D boxes, we propose a ray-based feature grouping module, which aggregates the point-wise features…”
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EDTER: Edge Detection with Transformer by Pu, Mengyang, Huang, Yaping, Liu, Yuming, Guan, Qingji, Ling, Haibin

“…Convolutional neural networks have made significant progresses in edge detection by progressively exploring the context and semantic features…”
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Detecting Camouflaged Object in Frequency Domain by Zhong, Yijie, Li, Bo, Tang, Lv, Kuang, Senyun, Wu, Shuang, Ding, Shouhong

“…Camouflaged object detection (COD) aims to identify objects that are perfectly embedded in their environment, which has various downstream applications in fields such as medicine, art, and agriculture…”
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Multimodal Token Fusion for Vision Transformers by Wang, Yikai, Chen, Xinghao, Cao, Lele, Huang, Wenbing, Sun, Fuchun, Wang, Yunhe

“…Many adaptations of transformers have emerged to address the single-modal vision tasks, where self-attention modules are stacked to handle input sources like…”
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A Keypoint-based Global Association Network for Lane Detection by Wang, Jinsheng, Ma, Yinchao, Huang, Shaofei, Hui, Tianrui, Wang, Fei, Qian, Chen, Zhang, Tianzhu

“…Lane detection is a challenging task that requires predicting complex topology shapes of lane lines and distinguishing different types of lanes simultaneously…”
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Dense Learning based Semi-Supervised Object Detection by Chen, Binghui, Li, Pengyu, Chen, Xiang, Wang, Biao, Zhang, Lei, Hua, Xian-Sheng

“…Semi-supervised object detection (SSOD) aims to facilitate the training and deployment of object detectors with the help of a large amount of unlabeled data…”
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BASNet: Boundary-Aware Salient Object Detection by Qin, Xuebin, Zhang, Zichen, Huang, Chenyang, Gao, Chao, Dehghan, Masood, Jagersand, Martin

“…Deep Convolutional Neural Networks have been adopted for salient object detection and achieved the state-of-the-art performance…”
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Zoom In and Out: A Mixed-scale Triplet Network for Camouflaged Object Detection by Pang, Youwei, Zhao, Xiaoqi, Xiang, Tian-Zhu, Zhang, Lihe, Lu, Huchuan

“…The recently proposed camouflaged object detection (COD) attempts to segment objects that are visually blended into their surroundings, which is extremely complex and difficult in real-world scenarios…”
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