Search Results - grouping and shape analysis; Vision applications and systems

Image Segmentation Using Text and Image Prompts by Luddecke, Timo, Ecker, Alexander

“… Here we propose a system that can generate image segmentations based on arbitrary prompts at test time…”
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FocalClick: Towards Practical Interactive Image Segmentation by Chen, Xi, Zhao, Zhiyan, Zhang, Yilei, Duan, Manni, Qi, Donglian, Zhao, Hengshuang

“…Interactive segmentation allows users to extract target masks by making positive/negative clicks. Although explored by many previous works, there is still a…”
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Class Similarity Weighted Knowledge Distillation for Continual Semantic Segmentation by Phan, Minh Hieu, Ta, The-Anh, Phung, Son Lam, Tran-Thanh, Long, Bouzerdoum, Abdesselam

“… Continual learning for semantic segmentation (CSS) is an emerging field in computer vision…”
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Sketching without Worrying: Noise-Tolerant Sketch-Based Image Retrieval by Bhunia, Ayan Kumar, Koley, Subhadeep, Khilji, Abdullah Faiz Ur Rahman, Sain, Aneeshan, Chowdhury, Pinaki Nath, Xiang, Tao, Song, Yi-Zhe

“…Sketching enables many exciting applications, notably, image retrieval. The fear-to-sketch problem (i.e., "I can't sketch…”
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LGT-Net: Indoor Panoramic Room Layout Estimation with Geometry-Aware Transformer Network by Jiang, Zhigang, Xiang, Zhongzheng, Xu, Jinhua, Zhao, Ming

“…3D room layout estimation by a single panorama using deep neural networks has made great progress. However, previous approaches can not obtain efficient…”
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End-to-End Compressed Video Representation Learning for Generic Event Boundary Detection by Li, Congcong, Wang, Xinyao, Wen, Longyin, Hong, Dexiang, Luo, Tiejian, Zhang, Libo

“…Generic event boundary detection aims to localize the generic, taxonomy-free event boundaries that segment videos into chunks. Existing methods typically…”
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A Versatile Multi-View Framework for LiDAR-based 3D Object Detection with Guidance from Panoptic Segmentation by Fazlali, Hamidreza, Xu, Yixuan, Ren, Yuan, Liu, Bingbing

“…3D object detection using LiDAR data is an indispensable component for autonomous driving systems…”
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SkinningNet: Two-Stream Graph Convolutional Neural Network for Skinning Prediction of Synthetic Characters by Mosella-Montoro, Albert, Ruiz-Hidalgo, Javier

“… on shape class and structure of the provided mesh. Whereas previous meth-ods pre-compute handcrafted features that relate the mesh and the skeleton or assume a fixed…”
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Generalizing Interactive Backpropagating Refinement for Dense Prediction Networks by Lin, Fanqing, Price, Brian, Martinez, Tony

“…As deep neural networks become the state-of-the-art approach in the field of computer vision for dense prediction tasks, many methods have been developed for automatic estimation of the target outputs…”
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ADeLA: Automatic Dense Labeling with Attention for Viewpoint Shift in Semantic Segmentation by Ren, Hanxiang, Yang, Yanchao, Wang, He, Shen, Bokui, Fan, Qingnan, Zheng, Youyi, Liu, C. Karen, Guibas, Leonidas

“…We describe a method to deal with performance drop in semantic segmentation caused by viewpoint changes within multi-camera systems, where temporally paired images are readily available…”
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Video K-Net: A Simple, Strong, and Unified Baseline for Video Segmentation by Li, Xiangtai, Zhang, Wenwei, Pang, Jiangmiao, Chen, Kai, Cheng, Guangliang, Tong, Yunhai, Loy, Chen Change

“…This paper presents Video K-Net, a simple, strong, and unified framework for fully end-to-end video panoptic seg-mentation. The method is built upon K-Net, a…”
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Finding Badly Drawn Bunnies by Yang, Lan, Pang, Kaiyue, Zhang, Honggang, Song, Yi-Zhe

“…As lovely as bunnies are, your sketched version would probably not do it justice (Fig. 1). This paper recognises this very problem and studies sketch quality…”
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Robust Histopathology Image Analysis: To Label or to Synthesize? by Hou, Le, Agarwal, Ayush, Samaras, Dimitris, Kurc, Tahsin M., Gupta, Rajarsi R., Saltz, Joel H.

“…Detection, segmentation and classification of nuclei are fundamental analysis operations in digital pathology…”
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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. Most of the previous works…”
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Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks by Fare Garnot, Vivien Sainte, Landrieu, Loic

“…Unprecedented access to multi-temporal satellite imagery has opened new perspectives for a variety of Earth observation tasks. Among them, pixel-precise…”
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Adaptive Pyramid Context Network for Semantic Segmentation by He, Junjun, Deng, Zhongying, Zhou, Lei, Wang, Yali, Qiao, Yu

“… Based on this analysis, this paper proposes Adaptive Pyramid Context Network (APCNet) for semantic segmentation…”
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Self-Supervised Shape Alignment for Sports Field Registration by Shi, Feng, Marchwica, Paul, Gamboa Higuera, Juan Camilo, Jamieson, Mike, Javan, Mehrsan, Siva, Parthipan

“… onto broadcast videos as a practical application. Rather then using any pairwise labelled data for training, we propose a self-supervised data mining method to train the registration network with a natural image and its edge map…”
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A Performance Evaluation of Correspondence Grouping Methods for 3D Rigid Data Matching by Yang, Jiaqi, Xian, Ke, Wang, Peng, Zhang, Yanning

“…) is a fundamental problem in 3D computer vision. While a number of correspondence selection methods have been proposed in recent years, their advantages and shortcomings remain unclear regarding different applications and perturbations…”
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Deep Metric Learning for Open World Semantic Segmentation by Cen, Jun, Yun, Peng, Cai, Junhao, Yu Wang, Michael, Liu, Ming

“…) objects, which is important for safety-critical applications such as autonomous driving…”
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Point Cloud Augmentation with Weighted Local Transformations by Kim, Sihyeon, Lee, Sanghyeok, Hwang, Dasol, Lee, Jaewon, Hwang, Seong Jae, Kim, Hyunwoo J.

“…Despite the extensive usage of point clouds in 3D vision, relatively limited data are available for training deep neural networks…”
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