Ultra-fine-grained image recognition (UFGIR) categorizes objects with extremely small differences between classes, such as distinguishing between cultivars within the same species, as opposed to species-level classification in fine-grained image recognition (FGIR). The difficulty of this task is exacerbated due to the scarcity of samples per category. To tackle these challenges we introduce a novel approach employing down-sampling inter-layer adapters in a parameter-efficient setting, where the backbone parameters are frozen and we only fine-tune a small set of additional modules. By integrating dual-branch down-sampling, we significantly reduce the number of parameters and floating-point operations (FLOPs) required, making our method highly efficient. Comprehensive experiments on ten datasets demonstrate that our approach obtains outstanding accuracy-cost performance, highlighting its potential for practical applications in resource-constrained environments. In particular, our method increases the average accuracy by at least 6.8\% compared to other methods in the parameter-efficient setting while requiring at least 123x less trainable parameters compared to current state-of-the-art UFGIR methods and reducing the FLOPs by 30\% in average compared to other methods.
Frame2Freq: Spectral Adapters for Fine-Grained Video Understanding
Institute for Artificial Intelligence, University of Stuttgart（斯图加特大学人工智能研究所）；University Hospital Heidelberg, Diagnostic and Interventional Radiology（海德堡大学医院放射诊断与介入科）；Intelligent Assistive Systems Lab, University of Hildesheim（希尔德斯海姆大学智能辅助系统实验室）
Fine-Grained Scene Image Classification with Modality-Agnostic Adapter
School of Computer Science, Fudan University（复旦大学计算机科学学院）；Innovation Center of Calligraphy and Painting Creation Technology, MCT（中国传媒书画创作技术创新中心）；Videt Technology（维德科技）
Fine-Grained VLM Fine-tuning via Latent Hierarchical Adapter Learning
Information Materials and Intelligent Sensing Laboratory of Anhui Province（安徽省信息材料与智能感知实验室）；Anhui Provincial Key Laboratory of Multimodal Cognitive Computation（安徽省多模态认知计算重点实验室）；School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）
Decoupling Spatio-Temporal Adapter for Fine-Grained Badminton Action Localization
School of Economics and Management, Beihang University（北京航空航天大学经济管理学院）；Key Laboratory of Data Intelligence and Management, Beihang University, Ministry of Industry and Information Technology（信息产业部北京航空航天大学数据智能与管理重点实验室）
FineCLIPER: Multi-modal Fine-grained CLIP for Dynamic Facial Expression Recognition with AdaptERs
School of Automation, Northwestern Polytechnical University（西北工业大学自动化学院）；The University of Hong Kong（香港大学）；Nanyang Technological University（南洋理工大学）；School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；Unmanned System Research Institute, Northwestern Polytechnical University（西北工业大学无人系统研究所）
Spatial-Temporal Decoupled Adapter for Micro-gesture Online Recognition
Hefei University of Technology（合肥工业大学）；United Arab Emirates University（阿联酋大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（人工智能研究院，合肥国家综合科学中心）；Anhui Evolution Technology Co., Ltd.（安徽进化科技有限公司）
In this paper, we propose a new adapter network for adapting a pre-trained deep neural network to a target domain with minimal computation. The proposed model, unidirectional thin adapter (UDTA), helps the classifier adapt to new data by providing auxiliary features that complement the backbone network. UDTA takes outputs from multiple layers of the backbone as input features but does not transmit any feature to the backbone. As a result, UDTA can learn without computing the gradient of the backbone, which saves computation for training significantly. In addition, since UDTA learns the target …
Variational Adapter for Cross-modal Similarity Representation
School of Remote Sensing and Information Engineering（遥感与信息工程学院）；Wuhan University（武汉大学）；School of Data Science and Engineering（数据科学与工程学院）；East China Normal University（华东师范大学）；State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing（测绘遥感信息工程国家重点实验室）
SAM3-Adapter: Efficient Adaptation of Segment Anything 3 for Camouflage Object Segmentation, Shadow Detection, and Medical Image Segmentation
KOKONI, Moxin (Huzhou) Tech. Co., LTD（摩西（湖州）科技有限公司）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；School of Information Engineering, Huzhou University（湖州大学信息工程学院）；School of Electrical and Electronic Engineering, Nanyang Technological University（新加坡南洋理工大学电子与电气工程学院）；College of Computing and Data Science, Nanyang Technological University（新加坡南洋理工大学计算与数据科学学院）；School of Information Science and Technology, University of Science and Technology of China（中国科学技术大学信息科学与技术学院）
Task-Adapter++: Task-specific Adaptation with Order-aware Alignment for Few-shot Action Recognition
School of Computer Science, Northwestern Polytechnical University（计算机科学学院，西北工业大学）；School of Electronics and Information, Northwestern Polytechnical University（电子与信息学院，西北工业大学）
Sparsity Hurts: Simple Linear Adapter Can Boost Generalized Category Discovery
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, China（计算机网络与信息集成重点实验室（东南大学），教育部，中国）
Dynamical Adapter Fusion: Constructing A Global Adapter for Pre-Trained Model-based Class-Incremental Learning
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China（中国科学院计算技术研究所）；University of Chinese Academy of Sciences, Beijing, China（中国科学院大学）
DSAA: Dual-Stage Attribute Activation for Fine-grained Open Vocabulary Detection
Beijing University of Posts and Telecommunications（北京邮电大学）；Beijing E-Hualu Information Technology Co., Ltd.（北京亿华鲁信息技术有限公司）；State Key Laboratory of General Artificial Intelligence, BIGAI, Beijing, China（通用人工智能国家重点实验室，BIGAI，北京，中国）
University College London（伦敦大学学院）；University of Edinburgh（爱丁堡大学）；Alan Turing Institute（艾伦·图灵研究所）；Niantic（Niantic公司）
Vision-language models such as CLIP are pretrained on large volumes of internet sourced image and text pairs, and have been shown to sometimes exhibit impressive zero- and low-shot image classification performance. However, due to their size, fine-tuning these models on new datasets can be prohibitively expensive, both in terms of the supervision and compute required. To combat this, a series of light-weight adaptation methods have been proposed to efficiently adapt such models when limited supervision is available. In this work, we show that while effective on internet-style datasets, even th…
D$^2$ST-Adapter: Disentangled-and-Deformable Spatio-Temporal Adapter for Few-shot Action Recognition
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Peng Cheng Laboratory（鹏城实验室）；Shenyang Institute of Automation, Chinese Academy of Sciences（沈阳自动化研究所，中国科学院）
Beyond Graph Model: Reliable VLM Fine-Tuning via Random Graph Adapter
Anhui Provincial Key Laboratory of Multimodal Cognitive Computation（安徽省级多模态认知计算重点实验室）；School of Computer Science and Technology（计算机科学与技术学院）；Anhui University（安徽大学）
IMAGGarment: Fine-Grained Garment Generation for Controllable Fashion Design
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；NExT++ Research Centre, National University of Singapore（新加坡国立大学NExT++研究中心）；College of Information Science and Technology and Artificial Intelligence, Nanjing Forestry University（南京林业大学信息科学与技术学院及人工智能学院）
