5 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…