1 citations · 1 across the 7 of their papers we have counts for
8 papers
CL-CLIP: CLIP-Based Continual Learning Framework with Cost-Volume Category Decoupling for Object Detection
Zihan Liu, Yuguang Yang, Shengjie Su +5
Continual Object Detection (COD) requires a detector to acquire new categories over time while preserving previously learned ones. This goal is closely related to open-vocabulary d…
Language Prompt vs. Image Enhancement: Boosting Object Detection With CLIP in Hazy Environments
Jian Pang, Bingfeng Zhang, Jin Wang +3
Object detection in hazy environments is challenging because degraded objects are nearly invisible and their semantics are weakened by environmental noise, making it difficult for…
Beyond Visual Cues: Leveraging General Semantics as Support for Few-Shot Segmentation
Jin Wang, Bingfeng Zhang, Jian Pang +3
Few-shot segmentation (FSS) aims to segment novel classes under the guidance of limited support samples by a meta-learning paradigm. Existing methods mainly mine references from su…
Unbiased Semantic Decoding with Vision Foundation Models for Few-shot Segmentation
Jin Wang, Bingfeng Zhang, Jian Pang +3
Few-shot segmentation has garnered significant attention. Many recent approaches attempt to introduce the Segment Anything Model (SAM) to handle this task. With the strong generali…
A Training-free Synthetic Data Selection Method for Semantic Segmentation
Hao Tang, Siyue Yu, Jian Pang +1
Training semantic segmenter with synthetic data has been attracting great attention due to its easy accessibility and huge quantities. Most previous methods focused on producing la…
Rethinking Prior Information Generation with CLIP for Few-Shot Segmentation
Jin Wang, Bingfeng Zhang, Jian Pang +2
Few-shot segmentation remains challenging due to the limitations of its labeling information for unseen classes. Most previous approaches rely on extracting high-level feature maps…
Adaptive Bidirectional Displacement for Semi-Supervised Medical Image Segmentation
Hanyang Chi, Jian Pang, Bingfeng Zhang +1
Consistency learning is a central strategy to tackle unlabeled data in semi-supervised medical image segmentation (SSMIS), which enforces the model to produce consistent prediction…
Hazy Re-ID: An Interference Suppression Model For Domain Adaptation Person Re-identification Under Inclement Weather Condition
Jian Pang, Dacheng Zhang, Huafeng Li +2
In a conventional domain adaptation person Re-identification (Re-ID) task, both the training and test images in target domain are collected under the sunny weather. However, in rea…