https://github.com/schlevik/sam
공식 코드Semantics Altering Modifications for Evaluating Comprehension in Machine Reading
Papers with Code 아카이브
Visual Question Answering (VQA)
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 9
- 평가표
- 76
- 보존된 평가 기록
- 725
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/schuture/deeptumorvqa
공식 코드https://github.com/sdc17/crossget
공식 코드CrossGET: Cross-Guided Ensemble of Tokens for Accelerating Vision-Language Transformers
https://github.com/sdc17/upop
공식 코드UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers
https://github.com/sdesabbata/geospatial-mechanistic-interpretability
공식 코드Geospatial Mechanistic Interpretability of Large Language Models
https://github.com/sdlzy/arc
공식 코드Joint Answering and Explanation for Visual Commonsense Reasoning
https://github.com/sergiotasconmorales/consistency_vqa
공식 코드Consistency-preserving Visual Question Answering in Medical Imaging
https://github.com/sergiotasconmorales/imp_vqa
공식 코드Logical Implications for Visual Question Answering Consistency
https://github.com/sergiotasconmorales/locvqa
공식 코드https://github.com/sergiotasconmorales/locvqallm
공식 코드Targeted Visual Prompting for Medical Visual Question Answering
https://github.com/servicenow/fm2ds
공식 코드https://github.com/shahin-01/vqa-ad
공식 코드Explaining Autonomous Driving Actions with Visual Question Answering
https://github.com/shailaja183/actionconceptlearning
공식 코드ActionCOMET: A Zero-shot Approach to Learn Image-specific Commonsense Concepts about Actions
https://github.com/shailaja183/clevr_hyp
공식 코드https://github.com/shailaja183/objectconceptlearning
공식 코드Help Me Identify: Is an LLM+VQA System All We Need to Identify Visual Concepts?
https://github.com/shankhanil006/ssl-vqa
공식 코드Knowledge Guided Semi-Supervised Learning for Quality Assessment of User Generated Videos
https://github.com/shankhanil006/vision
공식 코드https://github.com/shannonai/mrc-srl
공식 코드https://github.com/shantipriyap/hausavqa
공식 코드HaVQA: A Dataset for Visual Question Answering and Multimodal Research in Hausa Language
https://github.com/sharlinu/marc
공식 코드Investigating Relational State Abstraction in Collaborative MARL
https://github.com/sharonal10/langint
공식 코드https://github.com/shawnhuang497/medplib
공식 코드Towards a Multimodal Large Language Model with Pixel-Level Insight for Biomedicine
https://github.com/shengyuzhang/DeVLBert
공식 코드DeVLBert: Learning Deconfounded Visio-Linguistic Representations
https://github.com/shenyedepisa/rscmqa
공식 코드Copy-Move Forgery Detection and Question Answering for Remote Sensing Image
https://github.com/shi-labs/cumo
공식 코드CuMo: Scaling Multimodal LLM with Co-Upcycled Mixture-of-Experts
https://github.com/shi-labs/vcoder
공식 코드VCoder: Versatile Vision Encoders for Multimodal Large Language Models
https://github.com/shidaizi/remud
공식 코드HAIBU-ReMUD: Reasoning Multimodal Ultrasound Dataset and Model Bridging to General Specific Domains
https://github.com/shijx12/XNM-Net
공식 코드https://github.com/shikiw/modality-integration-rate
공식 코드Deciphering Cross-Modal Alignment in Large Vision-Language Models with Modality Integration Rate
https://github.com/shikras/shikra
공식 코드Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic
https://github.com/shiqichen17/adaptvis
공식 코드Why Is Spatial Reasoning Hard for VLMs? An Attention Mechanism Perspective on Focus Areas
https://github.com/shishicode/agot
공식 코드Soft-Prompting with Graph-of-Thought for Multi-modal Representation Learning
https://github.com/showlab/Q2A
공식 코드AssistQ: Affordance-centric Question-driven Task Completion for Egocentric Assistant
https://github.com/showlab/all-in-one
공식 코드https://github.com/showlab/clvqa
공식 코드Symbolic Replay: Scene Graph as Prompt for Continual Learning on VQA Task
https://github.com/showlab/lova3
공식 코드LOVA3: Learning to Visual Question Answering, Asking and Assessment
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.