We study the zero-shot Composed Image Retrieval (ZS-CIR) task, which is to retrieve the target image given a reference image and a description without training on the triplet datasets. Previous works generate pseudo-word tokens by projecting the reference image features to the text embedding space. However, they focus on the global visual representation, ignoring the representation of detailed attributes, e.g., color, object number and layout. To address this challenge, we propose a Knowledge-Enhanced Dual-stream zero-shot composed image retrieval framework (KEDs). KEDs implicitly models the attributes of the reference images by incorporating a database. The database enriches the pseudo-word tokens by providing relevant images and captions, emphasizing shared attribute information in various aspects. In this way, KEDs recognizes the reference image from diverse perspectives. Moreover, KEDs adopts an extra stream that aligns pseudo-word tokens with textual concepts, leveraging pseudo-triplets mined from image-text pairs. The pseudo-word tokens generated in this stream are explicitly aligned with fine-grained semantics in the text embedding space. Extensive experiments on widely used benchmarks, i.e. ImageNet-R, COCO object, Fashion-IQ and CIRR, show that KEDs outperforms previous zero-shot composed image retrieval methods.
Composed Object Retrieval: Object-level Retrieval via Composed Expressions
Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications, Southeast University, Ministry of Education, Jiangsu, China（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，江苏，中国）；Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE（穆罕默德·本·扎耶德人工智能大学（MBZUAI），阿布扎赫德，阿联酋）
Benchmarking Composed Image Retrieval for Applied Earth Observation
organization= Visual Recognition Group, Department of Cybernetics, Czech Technical University in Prague , country= Czechia；organization= Remote Sensing Laboratory, School of Rural, Surveying；Geoinformatics Engineering, National Technical University of Athens , country= Greece；organization= Institute of Informatics \& Telecommunications, National Centre for Scientific Research ``Demokritos'' , country= Greece；organization= Department of Informatics；Kapodistrian University of Athens , country= Greece
Composed Image Retrieval for Training-Free Domain Conversion
Czech Technical University in Prague（布拉格捷克技术大学）；National Technical University of Athens（雅典国家技术大学）；Institute of Advanced Research in Artificial Intelligence (IARAI)（高级人工智能研究所）
National Technical University of Athens（雅典国立技术大学）；Czech Technical University in Prague（布拉格捷克技术大学）；Institute of Advanced Research in Artificial Intelligence (IARAI)（高级人工智能研究所（IARAI））
DiCE-CIR: Direct Composition Learning for Efficient Zero-Shot Composed Image Retrieval
Institute of Information & Communications Technology Planning & Evaluation (IITP)（信息通信技术规划与评估研究所（IITP））；Department of Artificial Intelligence, Korea University（韩国大学人工智能系）
Video anomaly detection aims to find the events in a video that do not conform to the expected behavior. The prevalent methods mainly detect anomalies by snippet reconstruction or future frame prediction error. However, the error is highly dependent on the local context of the current snippet and lacks the understanding of normality. To address this issue, we propose to detect anomalous events not only by the local context, but also according to the consistency between the testing event and the knowledge about normality from the training data. Concretely, we propose a novel two-stream framewor…
Image2Sentence based Asymmetrical Zero-shot Composed Image Retrieval
University of Science and Technology of China（中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）
针对零样本组合图像检索中缺乏标注三元组及大型 VL 模型部署困难的问题，提出 ISA 框架，通过 Image2Sentence、非对称架构与蒸馏对齐提升检索准确率和效率。
TEMA: Anchor the Image, Follow the Text for Multi-Modification Composed Image Retrieval
School of Software, Shandong University（山东大学软件学院）；Department of Computing, Hong Kong Polytechnic University（香港理工大学计算机系）；School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）计算机科学与技术学院）
We propose a novel CNN architecture called ACTNET for robust instance image retrieval from large-scale datasets. Our key innovation is a learnable activation layer designed to improve the signal-to-noise ratio (SNR) of deep convolutional feature maps. Further, we introduce a controlled multi-stream aggregation, where complementary deep features from different convolutional layers are optimally transformed and balanced using our novel activation layers, before aggregation into a global descriptor. Importantly, the learnable parameters of our activation blocks are explicitly trained, together wi…
WISER: Wider Search, Deeper Thinking, and Adaptive Fusion for Training-Free Zero-Shot Composed Image Retrieval
Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所基础模型研究中心）；School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences（中国科学院大学先进交叉学科学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；National University of Singapore（新加坡国立大学）；Wuhan AI Research（武汉人工智能研究院）；Minzu University of China（民族大学）
Context-I2W: Mapping Images to Context-dependent Words for Accurate Zero-Shot Composed Image Retrieval
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Beijing Institute of Technology（北京理工大学）；University of Adelaide（阿德莱德大学）
Composed Image Retrieval with Text Feedback via Multi-grained Uncertainty Regularization
Sea-NExT Joint Lab, National University of Singapore（新加坡国立大学 Sea-NExT 联合实验室）；Tsinghua University（清华大学）；Faculty of Science and Technology, and Institute of Collaborative Innovation, University of Macau（澳门大学科技学院及协同创新研究院）
Czech Technical University in Prague（布拉格捷克理工大学）；Athena Research Center（雅典娜研究中心）；National Technical University of Athens（雅典国立技术大学）；Hellenic Robotics Center of Excellence（希腊机器人卓越中心）；IARAI
COMBINER: Composed Image Retrieval Guided by Attribute-based Neighbor Relations
School of Software, Shandong University（山东大学软件学院）；School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）计算机科学与技术学院）；Department of Data Science, City University of Hong Kong（香港城市大学数据科学系）；Department of Computer Science and Engineering, Southern University of Science and Technology（南方科技大学计算机科学与工程系）
MLLM-Guided VLM Fine-Tuning with Joint Inference for Zero-Shot Composed Image Retrieval
College of Computing and Data Science, Nanyang Technological University, Singapore（南洋理工大学计算机与数据科学学院）；Department of Computer Science, University of California, Los Angeles, USA（加州大学洛杉矶分校计算机科学系）；Sun Yat-sen University Shenzhen Campus, School of Cyber Science and Technology, Shenzhen, China（中山大学深圳校区信息科学与技术学院）
UniCVR: From Alignment to Reranking for Unified Zero-Shot Composed Visual Retrieval
Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；City University of Hong Kong（香港城市大学）；Southern University of Science and Technology（南方科技大学）；Pengcheng Laboratory（鹏城实验室）
The Zero-Shot Sketch-based Image Retrieval (ZS-SBIR) is a challenging task because of the large domain gap between sketches and natural images as well as the semantic inconsistency between seen and unseen categories. Previous literature bridges seen and unseen categories by semantic embedding, which requires prior knowledge of the exact class names and additional extraction efforts. And most works reduce domain gap by mapping sketches and natural images into a common high-level space using constructed sketch-image pairs, which ignore the unpaired information between images and sketches. To add…
DeliCIR: Memory-Guided Test-Time Deliberation via Multi-Agent Collaboration for Composed Image Retrieval
Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences)（计算机网络与信息安全部重点实验室，教育部，山东计算机科学中心（济南国家超算中心），青岛科技大学（山东科学院））；School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）；School of Artificial Intelligence, Mohamed bin Zayed University（Mohamed bin Zayed大学人工智能学院）
