Verifying the authenticity of AI-generated images presents a growing challenge on social media platforms these days. While vision-language models (VLMs) like CLIP outdo in multimodal representation, their capacity for AI-generated image classification is underexplored due to the absence of such labels during the pre-training process. This work investigates whether CLIP embeddings inherently contain information indicative of AI generation. A proposed pipeline extracts visual embeddings using a frozen CLIP model, feeds its embeddings to lightweight networks, and fine-tunes only the final classifier. Experiments on the public CIFAKE benchmark show the performance reaches 95% accuracy without language reasoning. Few-shot adaptation to curated custom with 20% of the data results in performance to 85%. A closed-source baseline (Gemini-2.0) has the best zero-shot accuracy yet fails on specific styles. Notably, some specific image types, such as wide-angle photographs and oil paintings, pose significant challenges to classification. These results indicate previously unexplored difficulties in classifying certain types of AI-generated images, revealing new and more specific questions in this domain that are worth further investigation.
Lightweight Prompt-Guided CLIP Adaptation for Monocular Depth Estimation
School of Engineering Science, Simon Fraser University（西蒙弗雷泽大学工程科学学院）；School of Information Science and Technology, Eastern Institute of Technology（东方理工学院信息科学与技术学院）
COOkeD: Ensemble-based OOD detection in the era of zero-shot CLIP
Aalborg University（奥尔堡大学）；ENSTA, Institut Polytechnique de Paris（法国国立高等先进技术学校，巴黎综合理工学院）；University of Barcelona（巴塞罗那大学）；Computer Vision Center（计算机视觉中心）
CLIP-PING: Boosting Lightweight Vision-Language Models with Proximus Intrinsic Neighbors Guidance
Kyung Hee University（庆熙大学）；The University of Danang—Vietnam-Korea University of Information and Communication Technology（岘港大学—越韩信息与通信技术大学）；Digital Science and Technology Institute（数字科学技术研究院）
DAIT: Distillation from Vision-Language Models to Lightweight Classifiers with Adaptive Intermediate Teacher Transfer
School of Computer and Electronic Information, Nanjing Normal University, Nanjing, China（南京师范大学计算机与电子信息学院）；Medical Artificial Intelligence Lab, Westlake University（西湖大学医学人工智能实验室）
Linear Alignment of Vision-language Models for Image Captioning
ELLIS Unit Linz（ELLIS林茨单元）；LIT AI Lab（LIT人工智能实验室）；Institute for Machine Learning（机器学习研究所）；JKU LIT SAL（约翰内斯·开普勒大学林茨软件应用实验室）；Johannes Kepler University（约翰内斯·开普勒大学）
Koo-Fu CLIP: Closed-Form Adaptation of Vision-Language Models via Fukunaga-Koontz Linear Discriminant Analysis
Visual Recognition Group（视觉识别组）；Department of Cybernetics（cybernetics系）；Faculty of Electrical Engineering（电气工程系）；Czech Technical University in Prague（布拉格捷克技术大学）
PE-CLIP: A Parameter-Efficient Fine-Tuning of Vision Language Models for Dynamic Facial Expression Recognition
Univ. BTU Cottbus-Senftenberg（科特布斯-森夫滕堡应用科学大学）；Sorbonne University Abu Dhabi（索邦大学阿布扎比分校）；Univ. Polytechnique Hauts-de-France（上法兰西理工大学）；Centrale Lille（里尔中央理工学院）；CNRS（法国国家科学研究中心）
CLIP-AUTT: Test-Time Personalization with Action Unit Prompting for Fine-Grained Video Emotion Recognition
LIVIA, ILLS, Dept. of Systems Engineering, ETS Montreal（LIVIA, ILLS, 系统工程学院, 蒙特利尔高等技术学院）；LIVIA, Dept. of Software and IT Engineering, ETS Montreal（LIVIA, 软件与IT工程学院, 蒙特利尔高等技术学院）；Dept. of Computer Science, École Polytechnique（计算机科学系, 巴黎综合理工学院）
HAC: Parameter-Efficient Hyperbolic Adaptation of CLIP for Zero-Shot VQA
Department of Computer Science, University of Verona（威尼斯大学计算机科学系）；EVS - Embedded Vision Systems Srl（嵌入式视觉系统公司）；AI for Good (AIGO), Istituto Italiano di Tecnologia（AI for Good（AIGO），意大利技术研究院）
CLIP-guided Federated Learning on Heterogeneous and Long-Tailed Data
Institute of Artificial Intelligence（人工智能研究院）；School of Informatics, Xiamen University（厦门大学信息学院）；East China Normal University（华东师范大学）；Chongqing Institute of East China Normal University（华东师范大学重庆研究院）
University of the Bundeswehr Munich（慕尼黑联邦国防军大学）；Institute for Distributed Intelligent Systems (VIS)（分布式智能系统研究所（VIS））
Current architectures for multi-modality tasks such as visual question answering suffer from their high complexity. As a result, these architectures are difficult to train and require high computational resources. To address these problems we present a CLIP-based architecture that does not require any fine-tuning of the feature extractors. A simple linear classifier is used on the concatenated features of the image and text encoder. During training an auxiliary loss is added which operates on the answer types. The resulting classification is then used as an attention gate on the answer class s…
Hong Kong Baptist University（香港浸会大学）；Zhejiang University（浙江大学）；NetEase Inc.（网易公司）
Image Captioning is a traditional vision-and-language task that aims to generate the language description of an image. Recent studies focus on scaling up the model size and the number of training data, which significantly increase the cost of model training. Different to these heavy-cost models, we introduce a lightweight image captioning framework (I-Tuning), which contains a small number of trainable parameters. We design a novel I-Tuning cross-attention module to connect the non-trainable pre-trained language decoder GPT2 and vision encoder CLIP-ViT. Since most parameters are not required t…
FAA-CLIP: Federated Adversarial Adaptation of CLIP
School of Artificial Intelligence, Guilin University of Electronic Technology（桂林电子科技大学人工智能学院）；College of Medicine, Taibah University（塔伊夫大学医学院）；College of Computer Science and Engineering, Jeddah University（吉达大学计算机科学与工程学院）；Ecole de Technologie Superieure（高等技术学院）
L-CLIPScore: a Lightweight Embedding-based Captioning Metric for Evaluating and Training
Key Laboratory of New Generation Artificial Intelligence Technology & Its Interdisciplinary Applications, (Southeast University),Ministry of Education（新一代人工智能技术及跨学科应用国家重点实验室，东南大学，教育部）；Alibaba Group（阿里巴巴集团）
Underwater Image Enhancement by Diffusion Model with Customized CLIP-Classifier
College of Engineering, Ocean University of China（中国海洋大学工程学院）；School of Information and Control Engineering, Qingdao University of Technology（青岛理工大学信息与控制工程学院）
State Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）
The existing action recognition methods are mainly based on clip-level classifiers such as two-stream CNNs or 3D CNNs, which are trained from the randomly selected clips and applied to densely sampled clips during testing. However, this standard setting might be suboptimal for training classifiers and also requires huge computational overhead when deployed in practice. To address these issues, we propose a new framework for action recognition in videos, called {\em Dynamic Sampling Networks} (DSN), by designing a dynamic sampling module to improve the discriminative power of learned clip-level…
Human activity recognition (HAR) is a machine learning task with important applications in healthcare especially in the context of home care of patients and older adults. HAR is often based on data collected from smart sensors, particularly smart home IoT devices such as smartphones, wearables and other body sensors. Deep learning techniques like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been used for HAR, both in centralized and federated settings. However, these techniques have certain limitations: RNNs cannot be easily parallelized, CNNs have the limitat…
