arXiv Computer Vision
RADC: Risk-Aware Dual Caching for Vision-Language Test-Time Adaptation
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Computer Vision
Aug 27
Towards Purified Multi-Label Test-Time Adaptation of Vision-Language Models
The paper introduces PuRF, a cache‑based method for multi‑label test‑time adaptation of vision‑language models. PuRF first performs region purification to reliably identify object‑specific regions, then uses these purified regions to build a discriminative, temporally refreshed cache that mitigates dominant‑label bias. Experiments show that PuRF outperforms existing methods, achieving a 4.05% mAP gain on ViT‑B/32 across five datasets.
arXiv AI
Jul 29
AGMark: Attention-Guided Dynamic Watermarking for Large Vision-Language Models
arXiv:2602. 09611v2 Announce Type: replace-cross Abstract: Watermarking has emerged as a pivotal solution for content traceability and intellectual property protection in large vision language models (LVLMs).
arXiv AI
Jun 8
SS-TPT: Stability and Suitability-Guided Test-Time Prompt Tuning for Adversarially Robust Vision-Language Models
arXiv:2606. 06943v1 Announce Type: cross Abstract: Vision-language models (VLMs) such as CLIP achieve strong zero-shot recognition but remain highly fragile under adversarial perturbations.
arXiv Computer Vision
Sep 1
GATE: Reliability-Gated Gaussian Evidence Fusion for Training-Free Test-Time Adaptation of Vision-Language Models
arXiv:2608.29395v1 Announce Type: new Abstract: Vision-language models such as CLIP and SigLIP provide strong zero-shot recognition, but their predictions can degrade when deployed on target data tha...
arXiv AI
Jun 2
STaR-KV: Spatio-Temporal Adaptive Re-weighting for KV Cache Compression in GUI Vision-Language Models
arXiv:2606. 01790v1 Announce Type: cross Abstract: Vision-language-model-based graphical user interface (GUI) agents have shown broad automation capabilities, yet deployment is bottlenecked by a key-value (KV) cache that grows linearly with interaction steps.
arXiv Computer Vision
Sep 7
Test-Time Adaptation via Cache Personalization for Facial Expression Recognition in Videos
The paper presents Test-Time Adaptation via Cache Personalization (TTA‑CaP), a gradient‑free, cache‑based method that personalizes vision‑language models for facial expression recognition in videos. TTA‑CaP uses three complementary caches—a personalized static cache, a positive target cache, and a negative target cache—controlled by a tri‑gate mechanism to prevent corruption and provide robust subject‑matched evidence. Experiments on BioVid, StressID, and BAH datasets show that TTA‑CaP outperforms state‑of‑the‑art test‑time adaptation methods while keeping computational and memory overhead low.