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/h-zhao1997/cobra
Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning
https://github.com/hadi-hosseini/t2i-fineeval
T2I-FineEval: Fine-Grained Compositional Metric for Text-to-Image Evaluation
https://github.com/hamedR96/User-VLM
https://github.com/hanhpt23/silvar
https://github.com/happen2me/cross-gnn
https://github.com/hengyuan-hu/bottom-up-attention-vqa
Modeling Relationships in Referential Expressions with Compositional Modular Networks
https://github.com/herobd/NAF_dataset
End-to-end Document Recognition and Understanding with Dessurt
https://github.com/hieunghia-pat/UIT-MCAN
Deep Modular Co-Attention Networks for Visual Question Answering
https://github.com/hj963434688/Faster_rcnn
https://github.com/hjjpku/dynamic_graph_caption
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/hlr/vln-trans
https://github.com/homebrewltd/ichigo
https://github.com/hs2k/pytorch-smoothgrad
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/huan123/py-fatser-rcnn
https://github.com/huang-research-group/InstructionGPT-4
InstructionGPT-4: A 200-Instruction Paradigm for Fine-Tuning MiniGPT-4
https://github.com/hudaAlamri/DSTC7-Audio-Visual-Scene-Aware-Dialog-AVSD-Challenge
End-to-End Audio Visual Scene-Aware Dialog using Multimodal Attention-Based Video Features
https://github.com/huggingface/transformers
LXMERT: Learning Cross-Modality Encoder Representations from Transformers
https://github.com/huofushuo/SID
Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding
https://github.com/hwanheelee1993/vilbertscore
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
https://github.com/iamNCJ/DUMA-pytorch-lightning
https://github.com/iammrhelo/pytorch-vqa-dan
Dual Attention Networks for Multimodal Reasoning and Matching
https://github.com/ibm/gcog
A Dataset and Architecture for Visual Reasoning with a Working Memory
https://github.com/ibrahimSouleiman/GuessWhat
GuessWhat?! Visual object discovery through multi-modal dialogue
https://github.com/idiap/fullgrad-saliency
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/iflytek/cino
https://github.com/iflytek/vle
An Empirical Study of Training End-to-End Vision-and-Language Transformers
https://github.com/ihaeyong/unbiased-sgg
Learning to Compose Dynamic Tree Structures for Visual Contexts
https://github.com/illume-unified-mllm/ILLUME_plus
ILLUME+: Illuminating Unified MLLM with Dual Visual Tokenization and Diffusion Refinement
https://github.com/impira/docquery
https://github.com/insigh/Faster-RCNN-Tensorflow
https://github.com/isaaccasm/GradCAM-keras
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/isaaccorley/resize-is-all-you-need
https://github.com/ismailuddin/gradcam-tensorflow-2
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/itanvir/gradcam
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/ithuanhuan/gpu-py3-faster-rcnn
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.