https://github.com/moramisu/qft
공식 코드Design as Desired: Utilizing Visual Question Answering for Multimodal Pre-training
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/moramisu/qft-miccai2024
공식 코드Design as Desired: Utilizing Visual Question Answering for Multimodal Pre-training
https://github.com/moreno98/gradbias
공식 코드GradBias: Unveiling Word Influence on Bias in Text-to-Image Generative Models
https://github.com/morty-xu/tcc-bench
공식 코드TCC-Bench: Benchmarking the Traditional Chinese Culture Understanding Capabilities of MLLMs
https://github.com/mosh0110/guiding-llm
공식 코드https://github.com/moured/refchartqa
공식 코드RefChartQA: Grounding Visual Answer on Chart Images through Instruction Tuning
https://github.com/mpsc-umbc/efficient-vision-language-models-a-survey
공식 코드https://github.com/mshahabsepehri/mediconfusion
공식 코드https://github.com/mshukor/EvALign-ICL
공식 코드https://github.com/mshukor/ep-alm
공식 코드eP-ALM: Efficient Perceptual Augmentation of Language Models
https://github.com/mshukor/ima-lmms
공식 코드https://github.com/mshukor/vicha
공식 코드Efficient Vision-Language Pretraining with Visual Concepts and Hierarchical Alignment
https://github.com/msiam/pixfoundation
공식 코드PixFoundation: Are We Heading in the Right Direction with Pixel-level Vision Foundation Models?
https://github.com/msiip/medm-vl
공식 코드https://github.com/msmilevski/nrl
공식 코드Applying recent advances in Visual Question Answering to Record Linkage
https://github.com/msu-video-group/msu_vqm_compression_benchmark
공식 코드AIM 2024 Challenge on Compressed Video Quality Assessment: Methods and Results
https://github.com/multimodal-interpretability/nnn
공식 코드Nearest Neighbor Normalization Improves Multimodal Retrieval
https://github.com/muyehuang/chartsketcher
공식 코드ChartSketcher: Reasoning with Multimodal Feedback and Reflection for Chart Understanding
https://github.com/mv-lab/videoai-speedrun
공식 코드AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results
https://github.com/naver-ai/clip4dm
공식 코드https://github.com/naver-ai/cream
공식 코드https://github.com/naver-ai/tablevqabench
공식 코드TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains
https://github.com/naver/posescript
공식 코드https://github.com/ncsoft/cap2qa
공식 코드https://github.com/nctu-eva-lab/antifakeprompt
공식 코드AntifakePrompt: Prompt-Tuned Vision-Language Models are Fake Image Detectors
https://github.com/nctu-eva-lab/vhd11k
공식 코드T2Vs Meet VLMs: A Scalable Multimodal Dataset for Visual Harmfulness Recognition
https://github.com/necla-ml/SNLI-VE
공식 코드Visual Entailment Task for Visually-Grounded Language Learning
https://github.com/neelabhsinha/vlm-selection-tasks-domains-knowledge-type
공식 코드https://github.com/nerel-ds/nerel-bio
공식 코드NEREL-BIO: A Dataset of Biomedical Abstracts Annotated with Nested Named Entities
https://github.com/neukg/robustmrc
공식 코드An Understanding-Oriented Robust Machine Reading Comprehension Model
https://github.com/nevermorelch/ccg
공식 코드Consistency of Compositional Generalization across Multiple Levels
https://github.com/nevermorelch/mscg
공식 코드Multi-Sourced Compositional Generalization in Visual Question Answering
https://github.com/nimrodshabtay/livexiv
공식 코드LiveXiv -- A Multi-Modal Live Benchmark Based on Arxiv Papers Content
https://github.com/nju-websoft/TSQA
공식 코드https://github.com/nju-websoft/adalogn
공식 코드AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading Comprehension
https://github.com/njunlp/SKER
공식 코드Synonym Knowledge Enhanced Reader for Chinese Idiom Reading Comprehension
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.