https://github.com/yu-gi-oh-leilei/trm-ml
공식 코드Text-Region Matching for Multi-Label Image Recognition with Missing Labels
Papers with Code 아카이브
Image Captioning
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- 평가표
- 34
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- 419
Image Captioning is the task of describing the content of an image in words. This task lies at the intersection of computer vision and natural language processing. Most image captioning systems use an encoder-decoder framework, where an input image is encoded into an intermediate representation of the information in the image, and then decoded into a descriptive text sequence. The most popular benchmarks are nocaps and COCO, and models are typically evaluated according to a BLEU or CIDER metric.
( Image credit: Reflective Decoding Network for Image Captioning, ICCV'19)
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/yuanezhou/cbtrans
공식 코드https://github.com/yuchenliu98/comm
공식 코드From CLIP to DINO: Visual Encoders Shout in Multi-modal Large Language Models
https://github.com/yueen-ma/awesome-vla
공식 코드https://github.com/yueqis/multilingual_visual_reasoning
공식 코드What Is Missing in Multilingual Visual Reasoning and How to Fix It
https://github.com/yuhangzang/cascadematch
공식 코드Semi-Supervised and Long-Tailed Object Detection with CascadeMatch
https://github.com/yuhangzang/contextdet
공식 코드Contextual Object Detection with Multimodal Large Language Models
https://github.com/yukw777/tglg
공식 코드Temporally-Grounded Language Generation: A Benchmark for Real-Time Vision-Language Models
https://github.com/yuliang-liu/monkey
공식 코드Monkey: Image Resolution and Text Label Are Important Things for Large Multi-modal Models
https://github.com/yutingli0606/vision-matters
공식 코드Vision Matters: Simple Visual Perturbations Can Boost Multimodal Math Reasoning
https://github.com/yuweiyin/ttida
공식 코드TTIDA: Controllable Generative Data Augmentation via Text-to-Text and Text-to-Image Models
https://github.com/yxgeee/MMT
공식 코드https://github.com/yxgeee/SDA
공식 코드Structured Domain Adaptation with Online Relation Regularization for Unsupervised Person Re-ID
https://github.com/yxgeee/SpCL
공식 코드Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID
https://github.com/yxgeee/VisDA-ECCV20
공식 코드Structured Domain Adaptation with Online Relation Regularization for Unsupervised Person Re-ID
https://github.com/yxuansu/magic
공식 코드Language Models Can See: Plugging Visual Controls in Text Generation
https://github.com/yzh0905/aecl-stc
공식 코드https://github.com/yzhan238/pieclass
공식 코드https://github.com/yzou2/CBST
공식 코드Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training
https://github.com/zanyarz/NeuralTwinsTalk
공식 코드https://github.com/zanyarz/zodiac
공식 코드https://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/zerojumpline/seval
공식 코드Learning Label Refinement and Threshold Adjustment for Imbalanced Semi-Supervised Learning
https://github.com/zeyuwang-zju/diffx
공식 코드https://github.com/zh460045050/v2l-tokenizer
공식 코드Beyond Text: Frozen Large Language Models in Visual Signal Comprehension
https://github.com/zhang-haojie/wesam
공식 코드https://github.com/zhangbaijin/From-Redundancy-to-Relevance
공식 코드From Redundancy to Relevance: Information Flow in LVLMs Across Reasoning Tasks
https://github.com/zhangye-zoe/dawn
공식 코드DAWN: Domain-Adaptive Weakly Supervised Nuclei Segmentation via Cross-Task Interactions
https://github.com/zhanyang-nwpu/rsvg-pytorch
공식 코드RSVG: Exploring Data and Models for Visual Grounding on Remote Sensing Data
https://github.com/zhaochongan/gfs-vl
공식 코드Generalized Few-shot 3D Point Cloud Segmentation with Vision-Language Model
https://github.com/zhaohengyuan1/genixer
공식 코드Genixer: Empowering Multimodal Large Language Models as a Powerful Data Generator
https://github.com/zhaoyucs/vsd
공식 코드Visual Spatial Description: Controlled Spatial-Oriented Image-to-Text Generation
https://github.com/zhegan27/Semantic_Compositional_Nets
공식 코드https://github.com/zhimin-c/bridge3d
공식 코드Bridging the Domain Gap: Self-Supervised 3D Scene Understanding with Foundation Models
https://github.com/zhiqic/gsrformer
공식 코드GSRFormer: Grounded Situation Recognition Transformer with Alternate Semantic Attention Refinement
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.