Instance Segmentation
Instance segmentation, a computer vision task, aims to identify and delineate individual objects within an image or point cloud, going beyond simple object detection by providing precise pixel-level masks. Current research emphasizes improving efficiency and accuracy, particularly in challenging scenarios like dense object arrangements, limited data, and noisy annotations; popular approaches involve transformer-based models, prototype-based methods, and techniques leveraging self-supervised learning or language-vision prompts. This field is crucial for diverse applications, including medical image analysis, autonomous driving, agricultural monitoring, and remote sensing, enabling automated analysis and improved decision-making in various domains.
Papers
EM-Paste: EM-guided Cut-Paste with DALL-E Augmentation for Image-level Weakly Supervised Instance Segmentation
Yunhao Ge, Jiashu Xu, Brian Nlong Zhao, Laurent Itti, Vibhav Vineet
Solve the Puzzle of Instance Segmentation in Videos: A Weakly Supervised Framework with Spatio-Temporal Collaboration
Liqi Yan, Qifan Wang, Siqi Ma, Jingang Wang, Changbin Yu
X-Paste: Revisiting Scalable Copy-Paste for Instance Segmentation using CLIP and StableDiffusion
Hanqing Zhao, Dianmo Sheng, Jianmin Bao, Dongdong Chen, Dong Chen, Fang Wen, Lu Yuan, Ce Liu, Wenbo Zhou, Qi Chu, Weiming Zhang, Nenghai Yu
AsyInst: Asymmetric Affinity with DepthGrad and Color for Box-Supervised Instance Segmentation
Siwei Yang, Longlong Jing, Junfei Xiao, Hang Zhao, Alan Yuille, Yingwei Li
LWSIS: LiDAR-guided Weakly Supervised Instance Segmentation for Autonomous Driving
Xiang Li, Junbo Yin, Botian Shi, Yikang Li, Ruigang Yang, Jianbing Shen
MEDIAR: Harmony of Data-Centric and Model-Centric for Multi-Modality Microscopy
Gihun Lee, Sangmook Kim, Joonkee Kim, Se-Young Yun