https://github.com/Cadene/murel.bootstrap.pytorch
공식 코드MUREL: Multimodal Relational Reasoning for Visual Question Answering
Papers with Code 아카이브
Visual Question Answering (VQA)
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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/Cadene/vqa.pytorch
공식 코드MUTAN: Multimodal Tucker Fusion for Visual Question Answering
https://github.com/Chat-3D/Chat-3D
공식 코드Chat-3D: Data-efficiently Tuning Large Language Model for Universal Dialogue of 3D Scenes
https://github.com/ChenRocks/UNITER
공식 코드https://github.com/China-UK-ZSL/ZS-F-VQA
공식 코드https://github.com/Cold-Winter/vqs
공식 코드https://github.com/CongSun-dlut/BioBERT-MRC
공식 코드Biomedical named entity recognition using BERT in the machine reading comprehension framework
https://github.com/ControlNet/HYDRA
공식 코드HYDRA: A Hyper Agent for Dynamic Compositional Visual Reasoning
https://github.com/CraftJarvis/ROCKET-1
공식 코드ROCKET-1: Mastering Open-World Interaction with Visual-Temporal Context Prompting
https://github.com/CrossmodalGroup/SSL-VQA
공식 코드Overcoming Language Priors with Self-supervised Learning for Visual Question Answering
https://github.com/Cyanogenoid/perm-optim
공식 코드Learning Representations of Sets through Optimized Permutations
https://github.com/Cyanogenoid/vqa-counting
공식 코드Learning to Count Objects in Natural Images for Visual Question Answering
https://github.com/DS3Lab/WordScape
공식 코드https://github.com/DYEvaLab/EvalMuse
공식 코드https://github.com/DancingSoul/NQ_BERT-DM
공식 코드Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension
https://github.com/Deanplayerljx/tab-vcr
공식 코드TAB-VCR: Tags and Attributes based Visual Commonsense Reasoning Baselines
https://github.com/DelTA-Lab-IITK/CCM-WACV
공식 코드https://github.com/Depth2World/VLADBench
공식 코드Fine-Grained Evaluation of Large Vision-Language Models in Autonomous Driving
https://github.com/DigitalPhonetics/reading-comprehension
공식 코드https://github.com/Disiok/poetry-seq2seq
공식 코드Hierarchical Attention: What Really Counts in Various NLP Tasks
https://github.com/DoubtedSteam/MM-GCoT
공식 코드Grounded Chain-of-Thought for Multimodal Large Language Models
https://github.com/DreadPiratePsyopus/Fine_Grained_Clf
공식 코드https://github.com/DriveVLA/OpenDriveVLA
공식 코드OpenDriveVLA: Towards End-to-end Autonomous Driving with Large Vision Language Action Model
https://github.com/Earth-Intelligence-Lab/vleo-bench
공식 코드Good at captioning, bad at counting: Benchmarking GPT-4V on Earth observation data
https://github.com/Evanwu1125/ChartCards
공식 코드ChartCards: A Chart-Metadata Generation Framework for Multi-Task Chart Understanding
https://github.com/Fangjun-Li/SpatialLM-StepGame
공식 코드https://github.com/FightingFighting/cross-modal-information-flow-in-MLLM
공식 코드Cross-modal Information Flow in Multimodal Large Language Models
https://github.com/FlamingHorizon/CMM-VR
공식 코드https://github.com/Flitternie/FMProgramming
공식 코드https://github.com/Francois-Aubet/EQuANt
공식 코드https://github.com/FrankZxShen/ATS
공식 코드Adversarial Training with OCR Modality Perturbation for Scene-Text Visual Question Answering
https://github.com/FuxiaoLiu/LRV-Instruction
공식 코드Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning
https://github.com/GZHU-DVL/CLIPVQA
공식 코드https://github.com/GanjinZero/RAMM
공식 코드RAMM: Retrieval-augmented Biomedical Visual Question Answering with Multi-modal Pre-training
https://github.com/GeWu-Lab/MUSIC-AVQA
공식 코드Learning to Answer Questions in Dynamic Audio-Visual Scenarios
https://github.com/GeekDream-x/IDOL
공식 코드IDOL: Indicator-oriented Logic Pre-training for Logical Reasoning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.