9 papers
AdaDINO: Pair-Aware In-Backbone Adaptation of Frozen DINO for Efficient Remote Sensing Change Detection
Xu Zhang, Xinqing Li, Jianpeng Xie +3
Vision foundation models (VFMs) such as DINO are pretrained for single-image representation, whereas remote sensing change detection requires reasoning over a bi-temporal pair. Exi…
LAD-COD: Language-Aligned Dense Perception for Camouflaged Object Detection
Shangye Song, Tianzhi Zhu, Syed Ariff Syed Hesham +2
Camouflaged object detection (COD) aims to segment objects that exhibit high visual similarity to their surroundings, which reduces foreground-background discriminability and weake…
LoViF 2026 Challenge on Real-World All-in-One Image Restoration: Methods and Results
Xiang Chen, Hao Li, Jiangxin Dong +54
This paper presents a review for the LoViF Challenge on Real-World All-in-One Image Restoration. The challenge aimed to advance research on real-world all-in-one image restoration…
Towards Joint Quantization and Token Pruning of Vision-Language Models
Xinqing Li, Xin He, Xindong Zhang +3
Deploying Vision-Language Models (VLMs) under aggressive low-bit inference remains challenging because inference cost is dominated by the long visual-token prefix during prefill an…
The Second Challenge on Cross-Domain Few-Shot Object Detection at NTIRE 2026: Methods and Results
Xingyu Qiu, Yuqian Fu, Jiawei Geng +70
Cross-domain few-shot object detection (CD-FSOD) remains a challenging problem for existing object detectors and few-shot learning approaches, particularly when generalizing across…
When W4A4 Breaks Camouflaged Object Detection: Token-Group Dual-Constraint Activation Quantization
Tianqi Li, Wenyu Fang, Xin He +3
Camouflaged object detection (COD) segments objects that intentionally blend with the background, so predictions depend on subtle texture and boundary cues. COD is often needed und…
Certainty Is Redundant: Token Sparsification for Efficient Camouflaged Object Detection with Vision Foundation Models
Yuhan Gao, Shuhao Kang, Xin He +3
Camouflaged object detection (COD) aims to segment objects that closely resemble their surrounding environments. Vision foundation models (VFMs) provide strong transferable represe…
Amped: Adaptive Multi-stage Non-edge Pruning for Edge Detection
Yuhan Gao, Xinqing Li, Xin He +4
Edge detection is a fundamental image analysis task that underpins numerous high-level vision applications. Recent advances in Transformer architectures have significantly improved…
Make It Up: Fake Images, Real Gains in Generalized Few-shot Semantic Segmentation
Guohuan Xie, Xin He, Dingying Fan +3
Generalized few-shot semantic segmentation (GFSS) is fundamentally limited by the coverage of novel-class appearances under scarce annotations. While diffusion models can synthesiz…