https://github.com/msmilevski/nrl
공식 코드Applying recent advances in Visual Question Answering to Record Linkage
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/multimodal-interpretability/nnn
공식 코드Nearest Neighbor Normalization Improves Multimodal Retrieval
https://github.com/naver-ai/clip4dm
공식 코드https://github.com/naver-ai/cream
공식 코드https://github.com/naver-ai/tablevqabench
공식 코드TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains
https://github.com/naver/posescript
공식 코드https://github.com/ncsoft/cap2qa
공식 코드https://github.com/nctu-eva-lab/antifakeprompt
공식 코드AntifakePrompt: Prompt-Tuned Vision-Language Models are Fake Image Detectors
https://github.com/nctu-eva-lab/vhd11k
공식 코드T2Vs Meet VLMs: A Scalable Multimodal Dataset for Visual Harmfulness Recognition
https://github.com/necla-ml/SNLI-VE
공식 코드Visual Entailment Task for Visually-Grounded Language Learning
https://github.com/neelabhsinha/vlm-selection-tasks-domains-knowledge-type
공식 코드https://github.com/nevermorelch/ccg
공식 코드Consistency of Compositional Generalization across Multiple Levels
https://github.com/nevermorelch/mscg
공식 코드Multi-Sourced Compositional Generalization in Visual Question Answering
https://github.com/nkkbr/vica
공식 코드Towards Visuospatial Cognition via Hierarchical Fusion of Visual Experts
https://github.com/nlp-waseda/traveling-across-languages
공식 코드Traveling Across Languages: Benchmarking Cross-Lingual Consistency in Multimodal LLMs
https://github.com/nlpcode/tubench
공식 코드TUBench: Benchmarking Large Vision-Language Models on Trustworthiness with Unanswerable Questions
https://github.com/nnaik39/commvqa
공식 코드CommVQA: Situating Visual Question Answering in Communicative Contexts
https://github.com/nnaik39/context-vqa
공식 코드Context-VQA: Towards Context-Aware and Purposeful Visual Question Answering
https://github.com/noagarcia/ArtVQA
공식 코드A Dataset and Baselines for Visual Question Answering on Art
https://github.com/nooralahzadeh/clg-vqa
공식 코드Improving the Cross-Lingual Generalisation in Visual Question Answering
https://github.com/nttmdlab-nlp/VisualMRC
공식 코드https://github.com/nttmdlab-nlp/slidevqa
공식 코드SlideVQA: A Dataset for Document Visual Question Answering on Multiple Images
https://github.com/nvlabs/omnidrive
공식 코드OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reasoning
https://github.com/nvlabs/vila
공식 코드https://github.com/nzantout/sort3d
공식 코드https://github.com/oceanhao/CoNav
공식 코드CoNav: Collaborative Cross-Modal Reasoning for Embodied Navigation
https://github.com/ofa-sys/ofa
공식 코드https://github.com/om-ai-lab/ZoomEye
공식 코드https://github.com/open-compass/vlmevalkit
공식 코드https://github.com/open-dataflow/vts-v
공식 코드Multi-Step Visual Reasoning with Visual Tokens Scaling and Verification
https://github.com/openai/evals
공식 코드https://github.com/openbmb/omnilmm
공식 코드RLAIF-V: Open-Source AI Feedback Leads to Super GPT-4V Trustworthiness
https://github.com/opencausalab/more
공식 코드Quantifying and Mitigating Unimodal Biases in Multimodal Large Language Models: A Causal Perspective
https://github.com/opendatalab/h2rsvlm
공식 코드VHM: Versatile and Honest Vision Language Model for Remote Sensing Image Analysis
https://github.com/opendatalab/vhm
공식 코드VHM: Versatile and Honest Vision Language Model for Remote Sensing Image Analysis
https://github.com/opendrivelab/drivelm
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.