https://github.com/cdancette/detect-shortcuts
공식 코드Beyond Question-Based Biases: Assessing Multimodal Shortcut Learning in 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/cdancette/rubi.bootstrap.pytorch
공식 코드https://github.com/chahatraj/Vignette
공식 코드VIGNETTE: Socially Grounded Bias Evaluation for Vision-Language Models
https://github.com/chantalmp/rad-restruct
공식 코드Rad-ReStruct: A Novel VQA Benchmark and Method for Structured Radiology Reporting
https://github.com/chaoyi-wu/radfm
공식 코드Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Data
https://github.com/chartgalaxy/chartgalaxy
공식 코드ChartGalaxy: A Dataset for Infographic Chart Understanding and Generation
https://github.com/chat-3d/chat-3d-v2
공식 코드Chat-Scene: Bridging 3D Scene and Large Language Models with Object Identifiers
https://github.com/chen-gx/mprompt
공식 코드MPrompt: Exploring Multi-level Prompt Tuning for Machine Reading Comprehension
https://github.com/chend-vl/instructocr
공식 코드https://github.com/chengtan9907/mc-cot
공식 코드https://github.com/chengyuehuang511/frames-vqa
공식 코드https://github.com/chenyugao-cs/sma
공식 코드https://github.com/chenzcv7/motor
공식 코드Towards Medical Artificial General Intelligence via Knowledge-Enhanced Multimodal Pretraining
https://github.com/child-play-neurips/child-play
공식 코드Show, Don't Tell: Evaluating Large Language Models Beyond Textual Understanding with ChildPlay
https://github.com/chojw/genb
공식 코드https://github.com/chrisc36/autobias
공식 코드Learning to Model and Ignore Dataset Bias with Mixed Capacity Ensembles
https://github.com/chrisc36/debias
공식 코드Don't Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases
https://github.com/christopher-beckham/clevr-mrt
공식 코드Visual Question Answering From Another Perspective: CLEVR Mental Rotation Tests
https://github.com/chunmeifeng/vqa4cir
공식 코드VQA4CIR: Boosting Composed Image Retrieval with Visual Question Answering
https://github.com/circleradon/tokenpacker
공식 코드https://github.com/clause-bielefeld/illusionofcompetence-vqa-explanations
공식 코드https://github.com/clear-nus/gpt-pddl
공식 코드Translating Natural Language to Planning Goals with Large-Language Models
https://github.com/clh124/LMM-PVQA
공식 코드https://github.com/clip-vil/CLIP-ViL
공식 코드https://github.com/cliport/cliport
공식 코드https://github.com/clips/clicr
공식 코드CliCR: A Dataset of Clinical Case Reports for Machine Reading Comprehension
https://github.com/clovaai/donut
공식 코드https://github.com/codefanw/flashsloth
공식 코드FlashSloth: Lightning Multimodal Large Language Models via Embedded Visual Compression
https://github.com/codexxxl/GraphVQA
공식 코드GraghVQA: Language-Guided Graph Neural Networks for Graph-based Visual Question Answering
https://github.com/codezakh/SIMLA
공식 코드Single-Stream Multi-Level Alignment for Vision-Language Pretraining
https://github.com/codezakh/dataenvgym
공식 코드DataEnvGym: Data Generation Agents in Teacher Environments with Student Feedback
https://github.com/codezakh/seltda
공식 코드https://github.com/coisini-glenda/wsdan-for-medical-visual-question-answering
공식 코드https://github.com/compai-lab/miccai-2024-junli
공식 코드Language Models Meet Anomaly Detection for Better Interpretability and Generalizability
https://github.com/computer-vision-in-the-wild/cvinw_readings
공식 코드Vision-Language Pre-training: Basics, Recent Advances, and Future Trends
https://github.com/concept-fusion/concept-fusion
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.