https://github.com/gorjanradevski/vsepp_tensorflow
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
Papers with Code 아카이브
Image Retrieval
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 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/gorkaydemir/DINOSAUR
https://github.com/gpt4life/alpagasus
https://github.com/gregor-ge/mblip
https://github.com/gtolias/rmac
Particular object retrieval with integral max-pooling of CNN activations
https://github.com/guilk/vlc
ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision
https://github.com/hanq0212/IIAE-Pytorch
Variational Interaction Information Maximization for Cross-domain Disentanglement
https://github.com/hanzhanggit/StackGAN-inception-model
Learning Deep Representations of Fine-grained Visual Descriptions
https://github.com/hanzhanggit/StackGAN-v2
Learning Deep Representations of Fine-grained Visual Descriptions
https://github.com/hasibzunair/peekaboo
https://github.com/hbwang1427/image_retrieval
What Is the Best Practice for CNNs Applied to Visual Instance Retrieval?
https://github.com/hengxyz/ExpressionGAN
ExprGAN: Facial Expression Editing with Controllable Expression Intensity
https://github.com/himanshunaidu/cnn_adnan_fruit
Medical Image Retrieval using Deep Convolutional Neural Network
https://github.com/hrsma2i/dataset-cfpd
https://github.com/hssip/fashionsap
Fashion IQ: A New Dataset Towards Retrieving Images by Natural Language Feedback
https://github.com/hthoai/image-text-matching
https://github.com/huggingface/transformers
ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision
https://github.com/hungyiwu/mixed-distance
Barcode Annotations for Medical Image Retrieval: A Preliminary Investigation
https://github.com/hwanheelee1993/vilbertscore
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
https://github.com/iammrhelo/pytorch-vqa-dan
Dual Attention Networks for Multimodal Reasoning and Matching
https://github.com/icoz69/DeepEMD
DeepEMD: Differentiable Earth Mover's Distance for Few-Shot Learning
https://github.com/idejie/SCAN
https://github.com/ilias-vrg/ilias
https://github.com/immuno121/audio_source_classification
https://github.com/innat/DOLG-TensorFlow
DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features
https://github.com/insikk/delf_enhanced
Large-Scale Image Retrieval with Attentive Deep Local Features
https://github.com/interestingzhuo/foodretreival
Smooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval
https://github.com/ipmi-icns-uke/sparsam
https://github.com/ivclab/cvs
On the Exploration of Incremental Learning for Fine-grained Image Retrieval
https://github.com/jandaldrop/landmark-recognition-challenge
Large-Scale Image Retrieval with Attentive Deep Local Features
https://github.com/janinethoma/learning1M
https://github.com/jankrepl/mildlyoverfitted
https://github.com/jenicek/asmk
Learning and aggregating deep local descriptors for instance-level recognition
https://github.com/jhgan00/image-retrieval-transformers
https://github.com/jiajunhua/facebookresearch-MUSE
Improving zero-shot learning by mitigating the hubness problem
https://github.com/jialinwu17/tmpimgs
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.