7 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…
When W4A4 Breaks Camouflaged Object Detection: Token-Group Dual-Constraint Activation Quantization
Tianqi Li, Wenyu Fang, Xin He +3
The paper proposes a post‑training 4‑bit activation quantization method for transformer‑based camouflaged object detection that mitigates token‑level range domination to preserve s…
Hyper-FSAD: Training-Free and Language-Free Few-Shot Anomaly Detection via Sparse Hyper Matching
Guohuan Xie, Xin He, Dingying Fan +2
Few-shot anomaly detection (FSAD) is particularly valuable when only a few normal images are available in a new target domain, while anomalous cases are rare, diverse, and difficul…
CERSA: Cumulative Energy-Retaining Subspace Adaptation for Memory-Efficient Fine-Tuning
Jingze Ge, Xue Geng, Yun Liu +6
To mitigate the memory constraints associated with fine-tuning large pre-trained models, existing parameter-efficient fine-tuning (PEFT) methods, such as LoRA, rely on low-rank upd…
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…