4 papers
MaST: Motion-aware Sparse Pipeline for Lightweight Object Tracking
Qingmao Wei, Fagui Liu, Dengke Zhang +2
Transformer-based object trackers are renowned for their strong performance, yet dense token processing often leads to prohibitive computational cost, limiting real-time deployment…
ModuSeg: Decoupling Object Discovery and Semantic Retrieval for Training-Free Weakly Supervised Segmentation
Qingze He, Fagui Liu, Dengke Zhang +2
Weakly supervised semantic segmentation aims to achieve pixel-level predictions using image-level labels. Existing methods typically entangle semantic recognition and object locali…
Exploring Token-Level Augmentation in Vision Transformer for Semi-Supervised Semantic Segmentation
Dengke Zhang, Quan Tang, Fagui Liu +2
Semi-supervised semantic segmentation has witnessed remarkable advancements in recent years. However, existing algorithms are based on convolutional neural networks and directly ap…
CorrCLIP: Reconstructing Patch Correlations in CLIP for Open-Vocabulary Semantic Segmentation
Dengke Zhang, Fagui Liu, Quan Tang
Open-vocabulary semantic segmentation aims to assign semantic labels to each pixel without being constrained by a predefined set of categories. While Contrastive Language-Image Pre…