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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/alibaba-damo-academy/VCBench
Benchmarking Multimodal Mathematical Reasoning with Explicit Visual Dependency
https://github.com/alibaba-damo-academy/spokennlp
Sequence Model with Self-Adaptive Sliding Window for Efficient Spoken Document Segmentation
https://github.com/alibaba/EasyNLP/tree/master/examples/blip_retrieval
https://github.com/alibaba/graphtranslator
https://github.com/allenai/Break
https://github.com/allenai/allenact
https://github.com/allenai/allennlp-models
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
https://github.com/allenai/break-evaluator
https://github.com/allenai/pythia
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/allenai/visprog
Visual Programming: Compositional visual reasoning without training
https://github.com/allenyummy/EHR_NER
https://github.com/alpha-innovator/chartvlm
StructChart: On the Schema, Metric, and Augmentation for Visual Chart Understanding
https://github.com/alpha-vllm/lumina-t2x
https://github.com/amenra/ranx
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/amitakamath/hard_positives
CoCa: Contrastive Captioners are Image-Text Foundation Models
https://github.com/andreiluca96/reversed-vqa
Visual Question Answering: Datasets, Algorithms, and Future Challenges
https://github.com/andy9705/SumGD
Mitigating Hallucinations in Large Vision-Language Models via Summary-Guided Decoding
https://github.com/anilkarabulut/copy-LeNet
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/anisha2102/docvqa
https://github.com/ankurtaly/Attributions
https://github.com/ankurtaly/Integrated-Gradients
https://github.com/antoine77340/howto100m
A Joint Sequence Fusion Model for Video Question Answering and Retrieval
https://github.com/aoidragon/pope
Evaluating Object Hallucination in Large Vision-Language Models
https://github.com/ap229997/Conditional-Batch-Norm
https://github.com/apugoneappu/ask_me_anything
Multi-modal Factorized Bilinear Pooling with Co-Attention Learning for Visual Question Answering
https://github.com/apugoneappu/vqa_visualise
Multi-modal Factorized Bilinear Pooling with Co-Attention Learning for Visual Question Answering
https://github.com/arjunakula/neurips2021
CLEVR-Ref+: Diagnosing Visual Reasoning with Referring Expressions
https://github.com/arunmallya/simple-vqa
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding
https://github.com/arya46/VQA_HieCoAtt
Hierarchical Question-Image Co-Attention for Visual Question Answering
https://github.com/ashishpatel26/GRADCAM-Tensorflow2-Visual-Explainable-AI
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/atabas/Heartbeat-Classification
ECG Heartbeat Classification: A Deep Transferable Representation
https://github.com/autonomousvision/carla_garage
https://github.com/aviatesk/street-feature-analysis
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/axiomlab/Cable
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/ayulockin/interpretabilitycnn
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.