PET-Adapter: Test-Time Domain Adaptation for Full and Limited-Angle PET Image Reconstruction
Institute of Imaging and Computer Vision, RWTH Aachen University, Germany（成像与计算机视觉研究所，亚琛工业大学，德国）；Machine Learning for Medical Data, Heinrich Heine University Düsseldorf, Germany（医学数据机器学习，杜伊斯堡-艾森大学，德国）
Rüveyda Yilmaz, Yuli Wu, Johannes Stegmaier, Volkmar Schulz
Positron Emission Tomography (PET) image reconstruction is inherently challenged by Poisson noise and physical degradation factors, which are further exacerbated in limited-angle acquisitions. While deep learning methods demonstrate promising performance, their generalization to unseen clinical data distributions remains limited without extensive retraining. We propose PET-Adapter, a test-time domain adaptation framework for generative PET reconstruction models pretrained solely on phantom data. Our method enables adaptation to clinical datasets with varying anatomies, tracers, and scanner configurations without requiring paired ground truth. PET-Adapter introduces layer-wise low-rank anatomical conditioning during adaptation and Ordered Subset Expectation Maximization-based warm-starting that initializes the generation from physics-informed reconstructions, reducing diffusion steps from 50 to 2 without compromising quality. Experiments across multiple clinical datasets demonstrate superior 3D reconstruction performance in both full-angle and limited-angle settings, highlighting the clinical feasibility and computational efficiency of the proposed approach.
In this paper, we focus on a less explored, but more realistic and complex problem of domain adaptation in LiDAR semantic segmentation. There is a significant drop in performance of an existing segmentation model when training (source domain) and testing (target domain) data originate from different LiDAR sensors. To overcome this shortcoming, we propose an unsupervised domain adaptation framework that leverages unlabeled target domain data for self-supervision, coupled with an unpaired mask transfer strategy to mitigate the impact of domain shifts. Furthermore, we introduce the gated adapter …
IBM Research - Haifa（IBM研究院（海法））；Technion - Haifa（以色列理工学院（海法））
Deep neural networks have demonstrated impressive performance in various machine learning tasks. However, they are notoriously sensitive to changes in data distribution. Often, even a slight change in the distribution can lead to drastic performance reduction. Artificially augmenting the data may help to some extent, but in most cases, fails to achieve model invariance to the data distribution. Some examples where this sub-class of domain adaptation can be valuable are various imaging modalities such as thermal imaging, X-ray, ultrasound, and MRI, where changes in acquisition parameters or acq…
Oxford Robotics Institute（牛津机器人研究所）；University of Oxford（牛津大学）
Modern models that perform system-critical tasks such as segmentation and localization exhibit good performance and robustness under ideal conditions (i.e. daytime, overcast) but performance degrades quickly and often catastrophically when input conditions change. In this work, we present a domain adaptation system that uses light-weight input adapters to pre-processes input images, irrespective of their appearance, in a way that makes them compatible with off-the-shelf computer vision tasks that are trained only on inputs with ideal conditions. No fine-tuning is performed on the off-the-shelf…
Efficient Parameter Adaptation for Multi-Modal Medical Image Segmentation and Prognosis
Department of Computer Vision, Mohamed bin Zayed University of Artificial Intelligence（计算机视觉系，Mohamed bin Zayed人工智能大学）；Department of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence（机器学习系，Mohamed bin Zayed人工智能大学）；Michigan State University（密歇根州立大学）；M42 Health（M42健康）
ReLoRA: Knowledge-Reusing Adaptation for Fast Rollout of Evolving LLM Services
School of Computer Science and Technology, University of Science and Technology of China（计算机科学与技术学院，中国科学技术大学）；Suzhou Institute for Advanced Research, University of Science and Technology of China（苏州先进研究院，中国科学技术大学）
ViDA: Homeostatic Visual Domain Adapter for Continual Test Time Adaptation
National Key Laboratory for Multimedia Information Processing（多媒体信息处理国家重点实验室）；The Chinese University of Hong Kong（香港中文大学）；Hong Kong University of Science and Technology（香港科技大学）；School of Computer Science Peking University（北京大学计算机学院）；AIRobotics
VL-PET: Vision-and-Language Parameter-Efficient Tuning via Granularity Control
The Chinese University of Hong Kong（香港中文大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Centre for Perceptual and Interactive Intelligence（感知与交互智能中心）
Come Together, But Not Right Now: A Progressive Strategy to Boost Low-Rank Adaptation
Southern University of Science（南方科技大学）；City University of Hong Kong（香港城市大学）；Zhejiang University（浙江大学）；Hong Kong University of Science（香港科学大学）；University of Surrey（萨里大学）；New York University（纽约大学）
Urban Waterlogging Detection: A Challenging Benchmark and Large-Small Model Co-Adapter
School of Microelectronics and Communication Engineering, Chongqing University（重庆大学微电子与通信工程学院）；Huawei Technologies Co., Ltd.（华为技术有限公司）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）
Context-Aware Adapter Tuning for Few-Shot Relation Learning in Knowledge Graphs
School of Computing and Information Systems, Singapore Management University（新加坡管理大学计算与信息系统学院）；Institute for Infocomm Research, A*STAR（新加坡科技研究局信息通信研究所）；A*STAR Centre for Frontier AI Research（新加坡科技研究局前沿人工智能研究中心）
Rethinking Inter-LoRA Orthogonality in Adapter Merging: Insights from Orthogonal Monte Carlo Dropout
University of Manchester（曼彻斯特大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；InstantX Team（InstantX团队）；University of Edinburgh（爱丁堡大学）；Aalto University（阿alto大学）
C-LoRA: Continual Low-Rank Adaptation for Pre-trained Visual Models
Institute of Intelligent Information Processing, Shanxi University（山西大学智能信息处理研究所）；Alliance Manchester Business School, The University of Manchester（曼彻斯特大学联盟曼彻斯特商学院）
Semantic Library Adaptation: LoRA Retrieval and Fusion for Open-Vocabulary Semantic Segmentation
The Good AI Lab；University of Toronto（多伦多大学）；KTH（瑞典皇家理工学院）；AMD（超威半导体公司）；Silo AI；King；Technical University of Munich（慕尼黑工业大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Google（谷歌公司）；ETH Zurich（苏黎世联邦理工学院）；University of Bologna（博洛尼亚大学）
Fine-tuning pre-trained Neural Machine Translation (NMT) models is the dominant approach for adapting to new languages and domains. However, fine-tuning requires adapting and maintaining a separate model for each target task. We propose a simple yet efficient approach for adaptation in NMT. Our proposed approach consists of injecting tiny task specific adapter layers into a pre-trained model. These lightweight adapters, with just a small fraction of the original model size, adapt the model to multiple individual tasks simultaneously. We evaluate our approach on two tasks: (i) Domain Adaptation…
