Papers with Code 아카이브
Image Retrieval
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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>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mop/bier
Deep Metric Learning with BIER: Boosting Independent Embeddings Robustly
https://github.com/mostafaramadann/Single-Image-3D-reconstruction
https://github.com/msfuxian/SEMICON
SEMICON: A Learning-to-hash Solution for Large-scale Fine-grained Image Retrieval
https://github.com/msight-tech/research-xbm
https://github.com/nalexai/hyperlib
https://github.com/nanzhaogang/contrib/tree/master/application/circle-loss
Circle Loss: A Unified Perspective of Pair Similarity Optimization
https://github.com/nashory/DeLF-pytorch
Large-Scale Image Retrieval with Attentive Deep Local Features
https://github.com/naver/deep-image-retrieval
Particular object retrieval with integral max-pooling of CNN activations
https://github.com/navervision/MemVir
SoftTriple Loss: Deep Metric Learning Without Triplet Sampling
https://github.com/neka-nat/pytorch-hdml
https://github.com/open-edge-platform/geti
https://github.com/open-edge-platform/training_extensions
https://github.com/opengvlab/internvl-mmdetseg
InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks
https://github.com/oskyhn/CNNs-Without-Borders
Working hard to know your neighbor's margins: Local descriptor learning loss
https://github.com/pandigreat/DELF
Large-Scale Image Retrieval with Attentive Deep Local Features
https://github.com/paullerner/viquae
WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning
https://github.com/pertusa/multilabelLogoRecognition
Multi-Label Logo Recognition and Retrieval based on Weighted Fusion of Neural Features
https://github.com/pksvision/Deep-WaveNet-Underwater-Image-Restoration
Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
https://github.com/postBG/CosMo.pytorch
https://github.com/priscillalui/StackGAN-Stories
Learning Deep Representations of Fine-grained Visual Descriptions
https://github.com/pshroff04/Dual_Path_CNN
Dual-Path Convolutional Image-Text Embeddings with Instance Loss
https://github.com/pwc-1/Paper-5/tree/main/nllb
NLLB-CLIP -- train performant multilingual image retrieval model on a budget
https://github.com/pwc-1/Paper-8/tree/main/altclip
AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities
https://github.com/pwc-1/Paper-8/tree/main/clvp
CLIP-Event: Connecting Text and Images with Event Structures
https://github.com/pwc-1/Paper-8/tree/main/dinov2
https://github.com/pwc-1/Paper-9/tree/main/2/nllb
NLLB-CLIP -- train performant multilingual image retrieval model on a budget
https://github.com/pwc-1/Paper-9/tree/main/instructblip
InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning
https://github.com/qianjinhao/circle-loss
Circle Loss: A Unified Perspective of Pair Similarity Optimization
https://github.com/qianlinjun/delf-pytorch
Large-Scale Image Retrieval with Attentive Deep Local Features
https://github.com/qiujihao19/artemis
https://github.com/rabiulcste/vqazero
https://github.com/rafiahmed40/stack-adverserial-network
Learning Deep Representations of Fine-grained Visual Descriptions
https://github.com/rajatkoner08/oodformer
https://github.com/ramanakshay/hierarchical-geoclip
https://github.com/raojay7/cnnimageretrieval-pytorch
CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.