Semantic Segmentation
Semantic segmentation, the task of assigning a semantic label to each pixel in an image, aims to achieve precise pixel-level scene understanding. Current research emphasizes improving accuracy and efficiency across diverse data modalities (RGB, depth, lidar, hyperspectral, and time series) and challenging conditions (low light, adverse weather, imbalanced datasets), often employing advanced architectures like transformers and diffusion models alongside innovative loss functions and training strategies. This field is crucial for numerous applications, including autonomous driving, medical image analysis, remote sensing, and robotics, driving advancements in both model robustness and interpretability.
Papers
Spatial Coherence Loss for Salient and Camouflaged Object Detection and Beyond
Ziyun Yang, Kevin Choy, Sina Farsiu
Separate and Conquer: Decoupling Co-occurrence via Decomposition and Representation for Weakly Supervised Semantic Segmentation
Zhiwei Yang, Kexue Fu, Minghong Duan, Linhao Qu, Shuo Wang, Zhijian Song
Oil Spill Drone: A Dataset of Drone-Captured, Segmented RGB Images for Oil Spill Detection in Port Environments
T. De Kerf, S. Sels, S. Samsonova, S. Vanlanduit
Spannotation: Enhancing Semantic Segmentation for Autonomous Navigation with Efficient Image Annotation
Samuel O. Folorunsho, William R. Norris
Weakly Supervised Co-training with Swapping Assignments for Semantic Segmentation
Xinyu Yang, Hossein Rahmani, Sue Black, Bryan M. Williams
Mitigating Distributional Shift in Semantic Segmentation via Uncertainty Estimation from Unlabelled Data
David S. W. Williams, Daniele De Martini, Matthew Gadd, Paul Newman
A Large-scale Evaluation of Pretraining Paradigms for the Detection of Defects in Electroluminescence Solar Cell Images
David Torpey, Lawrence Pratt, Richard Klein
Scribble Hides Class: Promoting Scribble-Based Weakly-Supervised Semantic Segmentation with Its Class Label
Xinliang Zhang, Lei Zhu, Hangzhou He, Lujia Jin, Yanye Lu