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/chenbys/GradCAM
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/chenguolin/DiffSplat
https://github.com/chenkangyang/paddle_visual_bert
VisualBERT: A Simple and Performant Baseline for Vision and Language
https://github.com/chenshaowei57/BMRC
Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet Extraction
https://github.com/chiahsuan156/ODSQA
https://github.com/chinesh/Guided_Grad_CAM
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/chirag26495/DAN_VQA
https://github.com/chrisc36/bottom-up-attention-vqa
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/christiancosgrove/cs767final
https://github.com/claws-lab/multimodal-robustness-xmai
An Empirical Study of Training End-to-End Vision-and-Language Transformers
https://github.com/cle-ros/RoutingNetworks
Routing Networks and the Challenges of Modular and Compositional Computation
https://github.com/clic-lab/ciff
Touchdown: Natural Language Navigation and Spatial Reasoning in Visual Street Environments
https://github.com/clvrai/relation-network-tensorflow
https://github.com/cnichkawde/MatchingNetwork
https://github.com/code-implementation1/Code7/tree/main/SDNet
SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
https://github.com/code-implementation1/Code9/tree/main/OCRNet
https://github.com/codelion/adaptive-classifier
https://github.com/cooelf/SG-Net
https://github.com/crockwell/cap3d
https://github.com/csitfun/logiqa2.0
LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning
https://github.com/csmiler/tf-faster-rcnn-cpu
https://github.com/cvlab-stonybrook/gcdr-gaze
Diffusion-Refined VQA Annotations for Semi-Supervised Gaze Following
https://github.com/cvlab-tohoku/Dense-CoAttention-Network
https://github.com/d-ailin/blind-faith-in-text
https://github.com/daixiangzi/Grad_Cam-pytorch-resnet50
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/daltonj/treccastweb
TREC CAsT 2019: The Conversational Assistance Track Overview
https://github.com/damo-nlp-sg/inf-clip
Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding
https://github.com/damo-nlp-sg/videollama3
Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding
https://github.com/dandelin/Dynamic-memory-networks-plus-Pytorch
Dynamic Memory Networks for Visual and Textual Question Answering
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/dave-fernandes/ECGClassifier
ECG Heartbeat Classification: A Deep Transferable Representation
https://github.com/daxiapazi/faster-rcnn
https://github.com/deep-spin/quati
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.