Medical imaging models often degrade when deployed at new clinical sites due to differences in imaging equipment, protocols, and patient populations. Test-time adaptation (TTA) addresses this by updating a pretrained model using only unlabeled target data, without access to source data. However, existing TTA methods were designed for single-label classification on natural image benchmarks, minimizing entropy uniformly across all samples without considering label dependencies. This overlooks a key property of multi-label medical imaging: pathologies do not occur independently but exhibit structured co-occurrence patterns. In this work, we propose Co-occurrence Weighted Adaptation (CoWA), which leverages disease co-occurrence patterns as a reliability signal for adaptation. CoWA estimates label co-occurrence structure from model predictions and downweights samples that deviate from expected patterns, enabling adaptation to rely more on consistent predictions while reducing the impact of noisy ones. We evaluate CoWA on chest X-ray benchmarks under domain shifts and demonstrate consistent improvements over established baselines.
Woo-Kyoung Jeong, Yujin Choi, Dongbin Kim et al.· 0 citations
As Vision-Language-Action (VLA) models continue to scale in the number of parameters, the computational cost and resource requirements for domain-specific fine-tuning have become significant barriers to practical robotic deployment. While Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA (Low-Rank Adaptation) offer a potential solution, they often fail to match the task success rates of their fully fine-tuned counterparts. In this paper, we propose a novel hierarchical VLA architecture that achieves state-of-the-art performance while maintaining high parameter efficiency. Our model decomposes control into a high-level System 2 for instruction-conditioned semantic context encoding—a frozen PaliGemma-3B backbone with 0.12B trainable LoRA parameters and a reactive System 1 for multimodal fusion and action generation. To optimize training efficiency, System 2 takes only a single egocentric image, while System 1 recovers missing context by integrating wrist-view images via ResNet-34 and proprioceptive state history encoded with a single linear projection layer. This information is fused through a Transformer Encoder, and final action trajectories are refined via a Transformer-parameterized conditional flow-matching decoder. To improve task performance, we generate diverse candidates by sampling from N independently initialized Gaussian noise vectors and using different numbers of denoising steps K per sample, and then select the executed action using a Cal-QL (Calibrated Q-Learning)-based critic. Evaluated on the standardized LIBERO benchmark, our proposed model achieved a 98.1% average success rate, outperforming contemporary fully trained models across all task suites. These results demonstrate that strategic architectural design can enable parameter-efficient models to exceed the performance of full-scale fine-tuning, offering a viable path for high-performance robotics under constrained computational resources.
Woo-Kyoung Jeong, Yongwoo Gu, June-sup Yi et al.· 2026 23rd International Conf...· 0 citations
