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/sunwei925/minimalisticvqa
공식 코드Analysis of Video Quality Datasets via Design of Minimalistic Video Quality Models
https://github.com/superagi/veagle
공식 코드https://github.com/svaishnavi411/multimodal-recurrence
공식 코드Multimodal fusion of imaging and genomics for lung cancer recurrence prediction
https://github.com/swadesh06/revisting_cropa
공식 코드https://github.com/swarupbehera/mAVQA
공식 코드https://github.com/swordlidev/evaluation-multimodal-llms-survey
공식 코드https://github.com/sxjyjay/lumen
공식 코드Lumen: Unleashing Versatile Vision-Centric Capabilities of Large Multimodal Models
https://github.com/syon-li/depwignn
공식 코드DepWiGNN: A Depth-wise Graph Neural Network for Multi-hop Spatial Reasoning in Text
https://github.com/szhang42/Contextual_dropout_release
공식 코드Contextual Dropout: An Efficient Sample-Dependent Dropout Module
https://github.com/szhang42/alignment_attention
공식 코드https://github.com/szzexpoi/AiR
공식 코드https://github.com/szzexpoi/rex
공식 코드https://github.com/taco-group/autotrust
공식 코드AutoTrust: Benchmarking Trustworthiness in Large Vision Language Models for Autonomous Driving
https://github.com/taco-group/re-align
공식 코드Re-Align: Aligning Vision Language Models via Retrieval-Augmented Direct Preference Optimization
https://github.com/taco-group/video-quality-assessment-a-comprehensive-survey
공식 코드https://github.com/tae2hee/coqah
공식 코드https://github.com/tahmedge/chart_lvlm_judge
공식 코드https://github.com/tanchongmin/arc-challenge
공식 코드An Approach to Solving the Abstraction and Reasoning Corpus (ARC) Challenge
https://github.com/tangjielong928/mqspn
공식 코드Multi-Grained Query-Guided Set Prediction Network for Grounded Multimodal Named Entity Recognition
https://github.com/tanjatang/DMVCR
공식 코드Cognitive Visual Commonsense Reasoning Using Dynamic Working Memory
https://github.com/taokz/biomedgpt
공식 코드BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks
https://github.com/taozeyi1990/laws
공식 코드LAWS: Look Around and Warm-Start Natural Gradient Descent for Quantum Neural Networks
https://github.com/tejasG53/vqa_mutant
공식 코드MUTANT: A Training Paradigm for Out-of-Distribution Generalization in Visual Question Answering
https://github.com/tencentarc/gvt
공식 코드What Makes for Good Visual Tokenizers for Large Language Models?
https://github.com/text-to-cadquery/text-to-cadquery
공식 코드Text-to-CadQuery: A New Paradigm for CAD Generation with Scalable Large Model Capabilities
https://github.com/tgc1997/RMN
공식 코드Learning to Discretely Compose Reasoning Module Networks for Video Captioning
https://github.com/thaolmk54/hcrn-videoqa
공식 코드Hierarchical Conditional Relation Networks for Video Question Answering
https://github.com/thisisus7/ing-vp
공식 코드https://github.com/thomaswei-cn/MC-CoT
공식 코드https://github.com/threesr/unifine
공식 코드UniFine: A Unified and Fine-grained Approach for Zero-shot Vision-Language Understanding
https://github.com/thu-huangzc/FFAA
공식 코드FFAA: Multimodal Large Language Model based Explainable Open-World Face Forgery Analysis Assistant
https://github.com/thudm/cogvlm
공식 코드https://github.com/thudm/cogvlm2
공식 코드CogVLM2: Visual Language Models for Image and Video Understanding
https://github.com/thudm/glm-4
공식 코드CogVLM2: Visual Language Models for Image and Video Understanding
https://github.com/thunlp-mt/cospace
공식 코드CoSpace: Benchmarking Continuous Space Perception Ability for Vision-Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.