https://github.com/pudumagico/kr2024
공식 코드Declarative Knowledge Distillation from Large Language Models for Visual Question Answering Datasets
Papers with Code 아카이브
Visual Question Answering (VQA)
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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Visual Question Answering (VQA) is a task in computer vision that involves answering questions about an image. The goal of VQA is to teach machines to understand the content of an image and answer questions about it in natural language.
Image Source: visualqa.org
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/pzyseere/metaspatial
공식 코드MetaSpatial: Reinforcing 3D Spatial Reasoning in VLMs for the Metaverse
https://github.com/pzzhang/VinVL
공식 코드VinVL: Revisiting Visual Representations in Vision-Language Models
https://github.com/q-future/q-align
공식 코드Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels
https://github.com/q-future/visual-question-answering-for-video-quality-assessment
공식 코드VQA$^2$: Visual Question Answering for Video Quality Assessment
https://github.com/qhnhynmm/viocrvqa
공식 코드https://github.com/qiantianwen/nuscenes-qa
공식 코드NuScenes-QA: A Multi-modal Visual Question Answering Benchmark for Autonomous Driving Scenario
https://github.com/qingzwang/GHA-ImageCaptioning
공식 코드https://github.com/qizekun/ShapeLLM
공식 코드ShapeLLM: Universal 3D Object Understanding for Embodied Interaction
https://github.com/qizhust/cmcl_vqa_pl
공식 코드Cross-Modal Contrastive Learning for Robust Reasoning in VQA
https://github.com/qmme/stablevqa
공식 코드StableVQA: A Deep No-Reference Quality Assessment Model for Video Stability
https://github.com/qualityassessment/bvqi
공식 코드Exploring Opinion-unaware Video Quality Assessment with Semantic Affinity Criterion
https://github.com/quiil/quiil_thompson_solution
공식 코드https://github.com/qwenlm/qwen-vl
공식 코드Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
https://github.com/qwenlm/qwen2-vl
공식 코드Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
https://github.com/qybc/medblip
공식 코드MedBLIP: Bootstrapping Language-Image Pre-training from 3D Medical Images and Texts
https://github.com/qyp2000/kvq
공식 코드KVQ: Boosting Video Quality Assessment via Saliency-guided Local Perception
https://github.com/rabiulcste/vqazero
공식 코드Investigating Prompting Techniques for Zero- and Few-Shot Visual Question Answering
https://github.com/rahular/ellipsis-baselines
공식 코드https://github.com/rajatkoner08/Graphhopper
공식 코드Graphhopper: Multi-Hop Scene Graph Reasoning for Visual Question Answering
https://github.com/rajeswar18/pix2shape
공식 코드Pix2Shape: Towards Unsupervised Learning of 3D Scenes from Images using a View-based Representation
https://github.com/ramprs/grad-cam
공식 코드Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/ranqiu92/NumNet
공식 코드NumNet: Machine Reading Comprehension with Numerical Reasoning
https://github.com/rbler1234/ost-bench
공식 코드OST-Bench: Evaluating the Capabilities of MLLMs in Online Spatio-temporal Scene Understanding
https://github.com/reallm-labs/infigui-r1
공식 코드InfiGUI-R1: Advancing Multimodal GUI Agents from Reactive Actors to Deliberative Reasoners
https://github.com/reml-group/alo
공식 코드https://github.com/reml-group/fortisavqa
공식 코드FortisAVQA and MAVEN: a Benchmark Dataset and Debiasing Framework for Robust Multimodal Reasoning
https://github.com/reml-group/music-avqa-r
공식 코드Look, Listen, and Answer: Overcoming Biases for Audio-Visual Question Answering
https://github.com/renmengye/imageqa-public
공식 코드https://github.com/rerv/uniadapter
공식 코드UniAdapter: Unified Parameter-Efficient Transfer Learning for Cross-modal Modeling
https://github.com/rese1f/MovieChat
공식 코드MovieChat: From Dense Token to Sparse Memory for Long Video Understanding
https://github.com/richard-peng-xia/mmed-rag
공식 코드MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language Models
https://github.com/richard-peng-xia/rule
공식 코드RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models
https://github.com/rickltt/event_detection
공식 코드https://github.com/riddickzhou/llm-graph-synthetic-reasoning
공식 코드Enhancing Logical Reasoning in Large Language Models through Graph-based Synthetic Data
https://github.com/rifkiaputri/idk-mrc
공식 코드IDK-MRC: Unanswerable Questions for Indonesian Machine Reading Comprehension
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.