https://github.com/illuin-tech/colpali
ColPali: Efficient Document Retrieval with Vision Language Models
Papers with Code 아카이브
Text Matching
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Matching a target text to a source text based on their meaning.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/illuin-tech/grouse
ColPali: Efficient Document Retrieval with Vision Language Models
https://github.com/illuin-tech/vidore-benchmark
ColPali: Efficient Document Retrieval with Vision Language Models
https://github.com/jastfkjg/semantic-matching
https://github.com/komiya-m/MirrorGAN
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/lichengunc/pretrain-vl-data
https://github.com/luzhixiu/radclip
RadCLIP: Enhancing Radiologic Image Analysis through Contrastive Language-Image Pre-training
https://github.com/microsoft/VSE_Gradient
Dissecting Deep Metric Learning Losses for Image-Text Retrieval
https://github.com/microsoft/xpretrain
Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers
https://github.com/naver-ai/pcme
https://github.com/necla-ml/SNLI-VE
https://github.com/nidhikamal-emb/DPR_repo
Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering
https://github.com/oxygenlu/ratlip
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/pengfeiliheu/mumc
Self-supervised vision-language pretraining for Medical visual question answering
https://github.com/pioneerAlpha/BanglaText2ImageGeneration
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/pl8787/DeepRank_PyTorch
https://github.com/pl8787/MatchPyramid-TensorFlow
https://github.com/priscillalui/StackGAN-Stories
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/reaganmozer/textmatch
https://github.com/rightlit/cycle-image-gan-rev
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/roxanasoto/AttGanESRGAN
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/senmaoy/recurrent-affine-transformation-for-text-to-image-synthesis
DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis
https://github.com/shmsw25/GraphRetriever
Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering
https://github.com/sidward14/Style-AttnGAN
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/super-zhangchao/learning-to-match
https://github.com/taki0112/AttnGAN-Tensorflow
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/taoxugit/AttnGAN
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/tobran/galip
DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis
https://github.com/towhee-io/towhee
https://github.com/ucsd-ml-arts/ml-art-final-jeffrey
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/vista-h/gpt-4v_social_media
CoCoT: Contrastive Chain-of-Thought Prompting for Large Multimodal Models with Multiple Image Inputs
https://github.com/vladsandulescu/hatefulmemes
https://github.com/woodfrog/vse_infty
https://github.com/xiaomin418/cfsum
https://github.com/zjukg/DUET
Structure-CLIP: Towards Scene Graph Knowledge to Enhance Multi-modal Structured Representations
https://github.com/zjukg/trustuqa
Structure-CLIP: Towards Scene Graph Knowledge to Enhance Multi-modal Structured Representations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.