https://github.com/qwenlm/qwen2.5-vl
Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
Papers with Code 아카이브
Visual Question Answering (VQA)
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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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/rabihchamas/dcls-gradcam-eval
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/rahulcoding/CheXNet
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/rayanramoul/GradCAM
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/reallm-labs/infiguiagent
InfiGUI-R1: Advancing Multimodal GUI Agents from Reactive Actors to Deliberative Reasoners
https://github.com/renmengye/rec-attend-public
https://github.com/rgmyr/tf-prosenet
ECG Heartbeat Classification: A Deep Transferable Representation
https://github.com/rickyHong/faster-rcnn-tensorflow-repl
https://github.com/ricolike/mmgnn_textvqa
Multi-Modal Graph Neural Network for Joint Reasoning on Vision and Scene Text
https://github.com/rishavbb/bottom_up_vqa
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/rishikmani/sort-of-clevr
https://github.com/ritvikshrivastava/ADL_VQA_Tensorflow2
Hierarchical Question-Image Co-Attention for Visual Question Answering
https://github.com/rmokady/clip_prefix_caption
Unified Vision-Language Pre-Training for Image Captioning and VQA
https://github.com/ronghanghu/lcgn
Language-Conditioned Graph Networks for Relational Reasoning
https://github.com/ronghanghu/n2nmn
Learning to Reason: End-to-End Module Networks for Visual Question Answering
https://github.com/ronghanghu/pythia
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/ronilp/mac-network-pytorch-gqa
https://github.com/rosinality/mac-network-pytorch
https://github.com/rosinality/relation-networks-pytorch
https://github.com/rs9000/VisualReasoning_MMnet
https://github.com/rshah240/conv_visualiser
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/rubenpt91/pfl-docvqa-competition
https://github.com/ruc-aimc-lab/nt2vr
A Joint Sequence Fusion Model for Video Question Answering and Retrieval
https://github.com/runpeidong/act
ShapeLLM: Universal 3D Object Understanding for Embodied Interaction
https://github.com/ruotianluo/DiscCaptioning
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/ruotianluo/GoogleConceptualCaptioning
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/ruotianluo/ImageCaptioning.pytorch
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/ruotianluo/Transformer_Captioning
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/ruotianluo/iep-ref
CLEVR-Ref+: Diagnosing Visual Reasoning with Referring Expressions
https://github.com/ruotianluo/neuraltalk2.pytorch
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/ruotianluo/self-critical.pytorch
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/ruxuan666/VQA_program
https://github.com/ryoasu/grad-cam
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/saccharomycetes/text-based-traffic-understanding
https://github.com/sahilkhose/FloodNet
FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding
https://github.com/sail-sg/dualformer
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.