https://github.com/dandelin/Dynamic-memory-networks-plus-Pytorch
Dynamic Memory Networks for Visual and Textual Question Answering
Papers with Code 아카이브
Visual Question Answering
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/dannofield/CarND-Extended-Kalman-Filter
No Blind Spots: Full-Surround Multi-Object Tracking for Autonomous Vehicles using Cameras & LiDARs
https://github.com/dar-tau/lra-benchmark
https://github.com/darkonhub/darkon
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/daxiapazi/faster-rcnn
https://github.com/deep-spin/quati
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/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
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/emrgnt-cmplxty/zero-shot-replication
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/ethanjperez/film
CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
https://github.com/ethz-privsec/superhuman-ai-consistency
https://github.com/ethz-spylab/superhuman-ai-consistency
https://github.com/exgc/r1v-free
https://github.com/facebookresearch/EgoTV
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
https://github.com/facebookresearch/meru
Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts
https://github.com/facebookresearch/mmf
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/facebookresearch/multimodal
MDETR -- Modulated Detection for End-to-End Multi-Modal Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.