https://github.com/yujielu10/llmscore
공식 코드LLMScore: Unveiling the Power of Large Language Models in Text-to-Image Synthesis Evaluation
Papers with Code 아카이브
Image Retrieval with Multi-Modal Query
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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The problem of retrieving images from a database based on a multi-modal (image- text) query. Specifically, the query text prompts some modification in the query image and the task is to retrieve images with the desired modifications.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/yunjinli/vxp
공식 코드VXP: Voxel-Cross-Pixel Large-scale Image-LiDAR Place Recognition
https://github.com/yuxie11/R2D2
공식 코드https://github.com/yxuansu/magic
공식 코드Language Models Can See: Plugging Visual Controls in Text Generation
https://github.com/zdou0830/meter
공식 코드An Empirical Study of Training End-to-End Vision-and-Language Transformers
https://github.com/zengyan-97/x-vlm
공식 코드Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts
https://github.com/zengyan-97/x2-vlm
공식 코드X$^2$-VLM: All-In-One Pre-trained Model For Vision-Language Tasks
https://github.com/zhangxinsong-nlp/XFM
공식 코드https://github.com/zhangxu0963/npc
공식 코드https://github.com/zhangxu0963/vsl
공식 코드Image-text Retrieval via Preserving Main Semantics of Vision
https://github.com/zhixiongz/clip4cmr
공식 코드https://github.com/zhjohnchan/sk-vg
공식 코드Advancing Visual Grounding with Scene Knowledge: Benchmark and Method
https://github.com/zhwwwww/attribute-comprehension-of-vlms
공식 코드Evaluating Attribute Comprehension in Large Vision-Language Models
https://github.com/zinengtang/perceiver_vl
공식 코드Perceiver-VL: Efficient Vision-and-Language Modeling with Iterative Latent Attention
https://github.com/zjukg/structure-clip
공식 코드Structure-CLIP: Towards Scene Graph Knowledge to Enhance Multi-modal Structured Representations
https://github.com/zzezze/neighborretr
공식 코드NeighborRetr: Balancing Hub Centrality in Cross-Modal Retrieval
https://huggingface.co/Idan0405/ClipMD
공식 코드Increasing Textual Context Size Boosts Medical Image-Text Matching
https://github.com/2023-MindSpore-1/ms-code-161
OPT: Omni-Perception Pre-Trainer for Cross-Modal Understanding and Generation
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/blip2
https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/blip
https://github.com/2024-MindSpore-1/Code2/tree/main/model-1/clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/AAA-Zheng/LG_ITM
https://github.com/AI4Patents/IMPACT
https://github.com/AlexMoreo/tensorflow-Text2Vis
Picture It In Your Mind: Generating High Level Visual Representations From Textual Descriptions
https://github.com/AndreaCossu/Relation-Network-PyTorch
https://github.com/AndreiMoraru123/ContextCollector
https://github.com/AndreiMoraru123/Watch-and-Tell
https://github.com/AndresPMD/Clip_CMR
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/AndresPMD/StacMR
https://github.com/AyanKumarBhunia/on-the-fly-FGSBIR
Sketch Less for More: On-the-Fly Fine-Grained Sketch Based Image Retrieval
https://github.com/AyanKumarBhunia/semisupervised-FGSBIR
More Photos are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrieval
https://github.com/BryanPlummer/cite
Learning Two-Branch Neural Networks for Image-Text Matching Tasks
https://github.com/CPJKU/audio_conditioned_unet
https://github.com/CSDN-AI7/image_caption
https://github.com/Cathy-t/HELLO_image
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.