https://github.com/bassyess/cfcd
공식 코드Coarse-to-Fine: Learning Compact Discriminative Representation for Single-Stage Image Retrieval
Papers with Code 아카이브
Image Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
- 56
- 보존된 평가 기록
- 372
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/beichenzbc/long-clip
공식 코드https://github.com/benywon/ChiQA
공식 코드ChiQA: A Large Scale Image-based Real-World Question Answering Dataset for Multi-Modal Understanding
https://github.com/berniebear/Multi-HT100M
공식 코드Multilingual Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer of Vision-Language Models
https://github.com/billpsomas/rscir
공식 코드https://github.com/billpsomas/simpool
공식 코드Keep It SimPool: Who Said Supervised Transformers Suffer from Attention Deficit?
https://github.com/binjiezhang/RACT_ICLR2022
공식 코드Hot-Refresh Model Upgrades with Regression-Alleviating Compatible Training in Image Retrieval
https://github.com/binyxu/univintruder
공식 코드One Surrogate to Fool Them All: Universal, Transferable, and Targeted Adversarial Attacks with CLIP
https://github.com/birdortyedi/image-retrieval-with-capsules
공식 코드https://github.com/bkong/MCNCC
공식 코드https://github.com/brandonhanx/fame-vil
공식 코드FAME-ViL: Multi-Tasking Vision-Language Model for Heterogeneous Fashion Tasks
https://github.com/brandonhanx/mmf
공식 코드FashionViL: Fashion-Focused Vision-and-Language Representation Learning
https://github.com/budnikm/aml
공식 코드https://github.com/buptlinfy/zse-sbir
공식 코드Zero-Shot Everything Sketch-Based Image Retrieval, and in Explainable Style
https://github.com/canhld94/daejeon520
공식 코드https://github.com/cap-ntu/Video-to-Retail-Platform
공식 코드Hysia: Serving DNN-Based Video-to-Retail Applications in Cloud
https://github.com/cdluminate/advrank
공식 코드https://github.com/cesc47/eufcc-cir
공식 코드EUFCC-CIR: a Composed Image Retrieval Dataset for GLAM Collections
https://github.com/cfm-uestc/DPQ
공식 코드Beyond Product Quantization: Deep Progressive Quantization for Image Retrieval
https://github.com/cfm-uestc/DRQ
공식 코드Deep Recurrent Quantization for Generating Sequential Binary Codes
https://github.com/cgcl-codes/badhash
공식 코드BadHash: Invisible Backdoor Attacks against Deep Hashing with Clean Label
https://github.com/chenmingxiang110/AugNet
공식 코드AugNet: End-to-End Unsupervised Visual Representation Learning with Image Augmentation
https://github.com/chenshunpeng/FoL
공식 코드Focus on Local: Finding Reliable Discriminative Regions for Visual Place Recognition
https://github.com/chiangsonw/cala
공식 코드CaLa: Complementary Association Learning for Augmenting Composed Image Retrieval
https://github.com/chris210634/metric-learning-using-contextual-similarity
공식 코드Supervised Metric Learning to Rank for Retrieval via Contextual Similarity Optimization
https://github.com/christofhenkel/kaggle-landmark-2021-1st-place
공식 코드Efficient large-scale image retrieval with deep feature orthogonality and Hybrid-Swin-Transformers
https://github.com/chuhaojin/text2poster-icassp-22
공식 코드https://github.com/chunmeifeng/sprc
공식 코드https://github.com/chunmeifeng/vqa4cir
공식 코드VQA4CIR: Boosting Composed Image Retrieval with Visual Question Answering
https://github.com/claws-lab/multimodal-robustness-xmai
공식 코드Cross-Modal Attribute Insertions for Assessing the Robustness of Vision-and-Language Learning
https://github.com/clip-italian/clip-italian
공식 코드Contrastive Language-Image Pre-training for the Italian Language
https://github.com/clovaai/assembled-cnn
공식 코드Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural Network
https://github.com/clovaai/embedding-expansion
공식 코드Embedding Expansion: Augmentation in Embedding Space for Deep Metric Learning
https://github.com/clovaai/symmetrical-synthesis
공식 코드https://github.com/cluel01/clip-branches
공식 코드CLIP-Branches: Interactive Fine-Tuning for Text-Image Retrieval
https://github.com/cly234/deepclustering-connr
공식 코드Contextually Affinitive Neighborhood Refinery for Deep Clustering
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.