Papers with Code 아카이브
Phrase Grounding
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 5
- 보존된 평가 기록
- 30
Given an image and a corresponding caption, the Phrase Grounding task aims to ground each entity mentioned by a noun phrase in the caption to a region in the image.
Source: Phrase Grounding by Soft-Label Chain Conditional Random Field
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/sinadumi/mmrr
공식 코드https://github.com/sled-group/world-to-words
공식 코드World-to-Words: Grounded Open Vocabulary Acquisition through Fast Mapping in Vision-Language Models
https://github.com/szuhvern/maco
공식 코드https://github.com/talshaharabany/what-is-where-by-looking
공식 코드What is Where by Looking: Weakly-Supervised Open-World Phrase-Grounding without Text Inputs
https://github.com/thunlp/pevl
공식 코드PEVL: Position-enhanced Pre-training and Prompt Tuning for Vision-language Models
https://github.com/vios-s/ldm-phrase-grounding
공식 코드Zero-Shot Medical Phrase Grounding with Off-the-shelf Diffusion Models
https://github.com/xiangchenchao/ddpn
공식 코드Rethinking Diversified and Discriminative Proposal Generation for Visual Grounding
https://github.com/youngfly11/LCMCG-PyTorch
공식 코드https://github.com/Adam1679/mutan-article-net
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding
https://github.com/AleDella/mdter_eval
MDETR -- Modulated Detection for End-to-End Multi-Modal Understanding
https://github.com/BryanPlummer/Two_branch_network
Revisiting Image-Language Networks for Open-ended Phrase Detection
https://github.com/Cadene/vqa.pytorch
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding
https://github.com/Cyanogenoid/vqa-counting
https://github.com/JoonSeongPark/vqa
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding
https://github.com/MarcBS/keras
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding
https://github.com/MindCode-4/code-1/tree/main/visual_bert
VisualBERT: A Simple and Performant Baseline for Vision and Language
https://github.com/MindCode-4/code-5/tree/main/visual_bert
VisualBERT: A Simple and Performant Baseline for Vision and Language
https://github.com/Seth-Park/MultimodalExplanations
https://github.com/YIKUAN8/Transformers-VQA
VisualBERT: A Simple and Performant Baseline for Vision and Language
https://github.com/ZephyrZhuQi/ssbaseline
https://github.com/allenai/pythia
https://github.com/arunmallya/simple-vqa
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding
https://github.com/brown-palm/ObjectPrompt
https://github.com/bryanplummer/flickr30k_entities
Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models
https://github.com/chenkangyang/paddle_visual_bert
VisualBERT: A Simple and Performant Baseline for Vision and Language
https://github.com/cszzshi/SimD
An Open and Comprehensive Pipeline for Unified Object Grounding and Detection
https://github.com/divelab/vqa-text
https://github.com/facebookresearch/mmf
https://github.com/facebookresearch/multimodal
MDETR -- Modulated Detection for End-to-End Multi-Modal Understanding
https://github.com/facebookresearch/pythia
https://github.com/gabegrand/adversarial-vqa
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding
https://github.com/gchhablani/multilingual-vqa
VisualBERT: A Simple and Performant Baseline for Vision and Language
https://github.com/iqua/m-dgt
https://github.com/jackroos/pythia
https://github.com/jnhwkim/cbp
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.