5 papers · 1 filter
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…
HCC-3D: Hierarchical Compensatory Compression for 98% 3D Token Reduction in Vision-Language Models
Liheng Zhang, Jin Wang, Hui Li +2
3D understanding has drawn significant attention recently, leveraging Vision-Language Models (VLMs) to enable multi-modal reasoning between point cloud and text data. Current 3D-VL…
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…