https://github.com/leolee99/CLIP_ITM
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
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Image Retrieval is a fundamental and long-standing computer vision task that involves finding images similar to a given query from a large database. It is often considered a form of fine-grained, instance-level classification. The task is integral to image recognition alongside classification and cross-modal retrieval. By leveraging visual similarity and other criteria, image retrieval enables users to efficiently discover relevant images, making it a crucial tool in applications such as search and recommendation.
Extending CLIP for Category-to-image Retrieval in E-commerce
<span style="color:grey; opacity: 0.6">( Image credit: DELF )</span>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
Sketchformer: Transformer-based Representation for Sketched Structure
FastReID: A Pytorch Toolbox for General Instance Re-identification
MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks
Let's Transfer Transformations of Shared Semantic Representations
Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval
Vision-Language Models in Remote Sensing: Current Progress and Future Trends
NetVLAD: CNN architecture for weakly supervised place recognition
Vision-Language Models in Remote Sensing: Current Progress and Future Trends
Circle Loss: A Unified Perspective of Pair Similarity Optimization
HADA: A Graph-based Amalgamation Framework in Image-text Retrieval
Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
Hybrid Style Siamese Network: Incorporating style loss in complementary apparels retrieval
M3P: Learning Universal Representations via Multitask Multilingual Multimodal Pre-training
MosAIc: Finding Artistic Connections across Culture with Conditional Image Retrieval
Classification is a Strong Baseline for Deep Metric Learning
BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis
Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks
Large-Scale Image Retrieval with Attentive Deep Local Features
OPT: Omni-Perception Pre-Trainer for Cross-Modal Understanding and Generation
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
Answering Visual-Relational Queries in Web-Extracted Knowledge Graphs
Particular object retrieval with integral max-pooling of CNN activations
mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.