Papers with Code 아카이브
Referring Expression
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 1
- 보존된 평가 기록
- 1
Referring expressions places a bounding box around the instance corresponding to the provided description and image.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/IDEA-Research/Grounded-Segment-Anything
Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection
https://github.com/ImperialNLP/BertGen
VL-BERT: Pre-training of Generic Visual-Linguistic Representations
https://github.com/JHKim-snu/GVCCI
https://github.com/JHKim-snu/PGA
https://github.com/MindSpore-scientific/code-7/tree/main/ImageGPT
https://github.com/OthersideAI/self-operating-computer
Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V
https://github.com/PaddlePaddle/PaddleMIX
Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection
https://github.com/SDLZY/VCR_Align
https://github.com/YIKUAN8/Transformers-VQA
https://github.com/YuankaiQi/REVERIE
REVERIE: Remote Embodied Visual Referring Expression in Real Indoor Environments
https://github.com/anthonyhuo/sam-dam-for-compositional-reasoning
Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection
https://github.com/arjunakula/neurips2021
CLEVR-Ref+: Diagnosing Visual Reasoning with Referring Expressions
https://github.com/byahn2/clevr_ref
CLEVR-Ref+: Diagnosing Visual Reasoning with Referring Expressions
https://github.com/camenduru/grounded-segment-anything-colab
Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection
https://github.com/casia-iva-lab/fastsam
https://github.com/cszzshi/SimD
An Open and Comprehensive Pipeline for Unified Object Grounding and Detection
https://github.com/dcdcvgroup/LADS-mindspore
Referring Expression Comprehension Using Language Adaptive Inference
https://github.com/ddupont808/gpt-4v-act
Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V
https://github.com/facebookresearch/multimodal
MDETR -- Modulated Detection for End-to-End Multi-Modal Understanding
https://github.com/fuqianya/ViLBERT-Paddle
https://github.com/futurulus/colors-in-context
Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding
https://github.com/huggingface/transformers
https://github.com/hzlbbfrog/generative-bim
Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection
https://github.com/idea-research/dino-x-api
Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection
https://github.com/idea-research/grounded-sam-2
Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection
https://github.com/jeremylinky/youtube-vln
Airbert: In-domain Pretraining for Vision-and-Language Navigation
https://github.com/joyhsu0504/ns3d
https://github.com/jules-samaran/vl-bert
VL-BERT: Pre-training of Generic Visual-Linguistic Representations
https://github.com/lichengunc/pretrain-vl-data
https://github.com/lichengunc/speaker_listener_reinforcer
A Joint Speaker-Listener-Reinforcer Model for Referring Expressions
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/longzw1997/Open-GroundingDino
Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.