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
CLIP with Quality Captions: A Strong Pretraining for Vision Tasks
Pavan Kumar Anasosalu Vasu, Hadi Pouransari, Fartash Faghri, Oncel Tuzel
Rethinking Scanning Strategies with Vision Mamba in Semantic Segmentation of Remote Sensing Imagery: An Experimental Study
Qinfeng Zhu, Yuan Fang, Yuanzhi Cai, Cheng Chen, Lei Fan
Enhancing Weakly Supervised Semantic Segmentation with Multi-modal Foundation Models: An End-to-End Approach
Elham Ravanbakhsh, Cheng Niu, Yongqing Liang, J. Ramanujam, Xin Li
SSA-Seg: Semantic and Spatial Adaptive Pixel-level Classifier for Semantic Segmentation
Xiaowen Ma, Zhenliang Ni, Xinghao Chen
Multi-Target Unsupervised Domain Adaptation for Semantic Segmentation without External Data
Yonghao Xu, Pedram Ghamisi, Yannis Avrithis
Context-Guided Spatial Feature Reconstruction for Efficient Semantic Segmentation
Zhenliang Ni, Xinghao Chen, Yingjie Zhai, Yehui Tang, Yunhe Wang