https://github.com/codefanw/flashsloth
공식 코드FlashSloth: Lightning Multimodal Large Language Models via Embedded Visual Compression
Papers with Code 아카이브
Visual Question Answering
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/codexxxl/GraphVQA
공식 코드GraghVQA: Language-Guided Graph Neural Networks for Graph-based Visual Question Answering
https://github.com/codezakh/SIMLA
공식 코드Single-Stream Multi-Level Alignment for Vision-Language Pretraining
https://github.com/codezakh/seltda
공식 코드https://github.com/coisini-glenda/wsdan-for-medical-visual-question-answering
공식 코드https://github.com/compai-lab/miccai-2024-junli
공식 코드Language Models Meet Anomaly Detection for Better Interpretability and Generalizability
https://github.com/computer-vision-in-the-wild/cvinw_readings
공식 코드Vision-Language Pre-training: Basics, Recent Advances, and Future Trends
https://github.com/concept-fusion/concept-fusion
공식 코드https://github.com/control-xl/medical-vision-langauge-transformer
공식 코드https://github.com/cooldawnant/infochartqa
공식 코드InfoChartQA: A Benchmark for Multimodal Question Answering on Infographic Charts
https://github.com/cpystan/wsi-vqa
공식 코드WSI-VQA: Interpreting Whole Slide Images by Generative Visual Question Answering
https://github.com/cratileo/d2r
공식 코드https://github.com/cse-ai-lab/RealCQA
공식 코드RealCQA: Scientific Chart Question Answering as a Test-bed for First-Order Logic
https://github.com/csebuetnlp/illusionvqa
공식 코드IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models
https://github.com/csuhan/onellm
공식 코드https://github.com/cvi-szu/facebench
공식 코드https://github.com/cvi-szu/qa-clims
공식 코드Question-Answer Cross Language Image Matching for Weakly Supervised Semantic Segmentation
https://github.com/czczup/vit-adapter
공식 코드https://github.com/czzzzzzzzzzzzzzzzz/vgat
공식 코드https://github.com/d-ailin/clip-guided-decoding
공식 코드https://github.com/dair-iitd/TourismQA
공식 코드Joint Spatio-Textual Reasoning for Answering Tourism Questions
https://github.com/dalcalab/voxel
공식 코드VoxelPrompt: A Vision-Language Agent for Grounded Medical Image Analysis
https://github.com/dali-does/clevr-math
공식 코드CLEVR-Math: A Dataset for Compositional Language, Visual and Mathematical Reasoning
https://github.com/damo-nlp-sg/vcd
공식 코드Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding
https://github.com/danielgordon10/thor-iqa-cvpr-2018
공식 코드https://github.com/danilo-perico/guided_navigation_qsr
공식 코드Guided Navigation from Multiple Viewpoints using Qualitative Spatial Reasoning
https://github.com/davidmascharka/tbd-nets
공식 코드Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning
https://github.com/dcdmllm/hyperllava
공식 코드HyperLLaVA: Dynamic Visual and Language Expert Tuning for Multimodal Large Language Models
https://github.com/ddw2aigroup2cqupt/pa-llava
공식 코드PA-LLaVA: A Large Language-Vision Assistant for Human Pathology Image Understanding
https://github.com/debymf/ipa_probing
공식 코드Grounding Natural Language Instructions: Can Large Language Models Capture Spatial Information?
https://github.com/declare-lab/emma-x
공식 코드https://github.com/declare-lab/lg-vqa
공식 코드https://github.com/declare-lab/llm-puzzletest
공식 코드https://github.com/declare-lab/puzzle-reasoning
공식 코드https://github.com/declare-lab/sas-vqa
공식 코드Self-Adaptive Sampling for Efficient Video Question-Answering on Image--Text Models
https://github.com/declare-lab/sealing
공식 코드Self-Adaptive Sampling for Efficient Video Question-Answering on Image--Text Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.