https://github.com/deepopinion/anls-star-metric
Layout and Task Aware Instruction Prompt for Zero-shot Document Image 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/deepopinion/anls_star_metric
Layout and Task Aware Instruction Prompt for Zero-shot Document Image Question Answering
https://github.com/dengyh16code/MQA_dataset
https://github.com/deshanadesai/VQA-DataAugmentation
Show, Ask, Attend, and Answer: A Strong Baseline For Visual Question Answering
https://github.com/dfcf93/MSMARCO
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/dhruvyad/MultimodalGame
ShapeWorld - A new test methodology for multimodal language understanding
https://github.com/dialogtekgeek/AudioVisualSceneAwareDialog
End-to-End Audio Visual Scene-Aware Dialog using Multimodal Attention-Based Video Features
https://github.com/digirl-agent/digirl
https://github.com/dimlight13/MU_SC_for_VQA
Task-Oriented Multi-User Semantic Communications for VQA Task
https://github.com/dinhvietcuong1996/icme25-inova
https://github.com/dlvuldet/primevul
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
https://github.com/doc-doc/NExT-OE
NExT-QA:Next Phase of Question-Answering to Explaining Temporal Actions
https://github.com/dongping-chen/mllm-as-a-judge
https://github.com/dsl-lab/fvmd-frechet-video-motion-distance
Fréchet Video Motion Distance: A Metric for Evaluating Motion Consistency in Videos
https://github.com/dtanoglidis/DeepShadows
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/dukelin95/vqa_pytorch
Show, Ask, Attend, and Answer: A Strong Baseline For Visual Question Answering
https://github.com/dvlab-research/MGM
Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models
https://github.com/eclique/keras-gradcam
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/edithal-14/DMN-Novelty
Dynamic Memory Networks for Visual and Textual Question Answering
https://github.com/ellenzhuwang/implicit_vkood
https://github.com/emnlp-2021-g2r/ParlAI
https://github.com/emrgnt-cmplxty/zero-shot-replication
https://github.com/erekgit/parlai
https://github.com/eternityyw/tram-benchmark
https://github.com/ethancaballero/Improved-Dynamic-Memory-Networks-DMN-plus
Dynamic Memory Networks for Visual and Textual Question Answering
https://github.com/ethz-privsec/superhuman-ai-consistency
https://github.com/ethz-spylab/superhuman-ai-consistency
https://github.com/ewrfcas/Reinforced-Mnemonic-Reader
Reinforced Mnemonic Reader for Machine Reading Comprehension
https://github.com/exgc/r1v-free
https://github.com/facebookresearch/EgoTV
https://github.com/facebookresearch/House3D
https://github.com/facebookresearch/ParlAI
https://github.com/facebookresearch/chameleon
https://github.com/facebookresearch/corefnmn
Visual Coreference Resolution in Visual Dialog using Neural Module Networks
https://github.com/facebookresearch/data2vec_vision
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
https://github.com/facebookresearch/grid-feats-vqa
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.