https://github.com/lalithjets/surgical_vqa
VisualBERT: A Simple and Performant Baseline for Vision and Language
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/linhuixiao/awesome-visual-grounding
https://github.com/linhuixiao/clip-vg
https://github.com/linhuixiao/hivg
https://github.com/linhuixiao/oneref
https://github.com/longbai1006/surgical-vqla
VisualBERT: A Simple and Performant Baseline for Vision and Language
https://github.com/pwc-1/Paper-9/tree/main/5/visual_bert
VisualBERT: A Simple and Performant Baseline for Vision and Language
https://github.com/rabiulcste/vqazero
Kosmos-2: Grounding Multimodal Large Language Models to the World
https://github.com/ronghanghu/natural-language-object-retrieval
https://github.com/ronghanghu/pythia
https://github.com/rsCPSyEu/ovd_cod
https://github.com/social-ai-studio/matk
VisualBERT: A Simple and Performant Baseline for Vision and Language
https://github.com/uclanlp/visualbert
VisualBERT: A Simple and Performant Baseline for Vision and Language
https://github.com/vuhoangminh/vqa_medical
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding
https://github.com/yikang-li/iqan
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding
https://github.com/zhanyang-nwpu/rsvg-pytorch
Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.