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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/longmalongma/desa2va
공식 코드Decoupled Seg Tokens Make Stronger Reasoning Video Segmenter and Grounder
https://github.com/lskdream/gamival
공식 코드GAMIVAL: Video Quality Prediction on Mobile Cloud Gaming Content
https://github.com/ltgoslo/norquad
공식 코드https://github.com/ltu-machine-learning/idocvqa
공식 코드https://github.com/luciusssss/how-many-answers
공식 코드How Many Answers Should I Give? An Empirical Study of Multi-Answer Reading Comprehension
https://github.com/luciusssss/why-learn-shortcut
공식 코드https://github.com/luciusssss/xkbqa-as-mrc
공식 코드Cross-Lingual Question Answering over Knowledge Base as Reading Comprehension
https://github.com/lujunru/rolemrc
공식 코드RoleMRC: A Fine-Grained Composite Benchmark for Role-Playing and Instruction-Following
https://github.com/luka-group/causal-view-of-entity-bias
공식 코드https://github.com/lunaproject22/causalchaos
공식 코드https://github.com/luodian/otter
공식 코드Otter: A Multi-Modal Model with In-Context Instruction Tuning
https://github.com/luogen1996/laconvnet
공식 코드Towards Language-guided Visual Recognition via Dynamic Convolutions
https://github.com/luogen1996/llava-hr
공식 코드Feast Your Eyes: Mixture-of-Resolution Adaptation for Multimodal Large Language Models
https://github.com/luohongyin/RGX
공식 코드https://github.com/luomancs/alternative_answer_set
공식 코드https://github.com/luomancs/remuq
공식 코드https://github.com/luomancs/retriever_reader_for_okvqa
공식 코드Weakly-Supervised Visual-Retriever-Reader for Knowledge-based Question Answering
https://github.com/lupantech/InterGPS
공식 코드Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning
https://github.com/lupantech/ScienceQA
공식 코드Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering
https://github.com/lupantech/dual-mfa-vqa
공식 코드https://github.com/lupantech/iconqa
공식 코드IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning
https://github.com/lupantech/rvqa
공식 코드R-VQA: Learning Visual Relation Facts with Semantic Attention for Visual Question Answering
https://github.com/lvapeab/nmt-keras
공식 코드NMT-Keras: a Very Flexible Toolkit with a Focus on Interactive NMT and Online Learning
https://github.com/lx709/vrsbench
공식 코드VRSBench: A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding
https://github.com/lxucs/multilingual-mrc-isdg
공식 코드Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-sentence Dependency Graph
https://github.com/lyan62/FoodieQA
공식 코드FoodieQA: A Multimodal Dataset for Fine-Grained Understanding of Chinese Food Culture
https://github.com/macehil/nesy-asp-vqa-pipeline
공식 코드https://github.com/machine-teaching-group/tmlr2024_neurtasksyn
공식 코드https://github.com/magic-research/PLLaVA
공식 코드PLLaVA : Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning
https://github.com/mainlp/SYGAR
공식 코드https://github.com/mair-lab/mapl
공식 코드https://github.com/maitreyapatel/cripp-vqa
공식 코드CRIPP-VQA: Counterfactual Reasoning about Implicit Physical Properties via Video Question Answering
https://github.com/manga109/mangalmm
공식 코드MangaVQA and MangaLMM: A Benchmark and Specialized Model for Multimodal Manga Understanding
https://github.com/manoja328/tallyqacode
공식 코드https://github.com/mao1207/BioMed-VITAL
공식 코드Biomedical Visual Instruction Tuning with Clinician Preference Alignment
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