https://github.com/Lucas2012/ProbabilisticNeuralProgrammedNetwork
CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
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/LucasElbert/GradCam
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/Lynda-Starkus/Abnormal_ECG_Myocardial_infraction_cnn
ECG Heartbeat Classification: A Deep Transferable Representation
https://github.com/MS-P3/code3/tree/main/instructblip
InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning
https://github.com/MS-P3/code3/tree/main/internlm
https://github.com/MS-P3/code3/tree/main/layoutlmv2
LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
https://github.com/MS-P3/code5/tree/main/cogvlm
https://github.com/MS-P3/code7/tree/main/van
VANE-Bench: Video Anomaly Evaluation Benchmark for Conversational LMMs
https://github.com/MarSaKi/NvEM
https://github.com/MarcBS/keras
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding
https://github.com/MarkHershey/arxiv-dl
https://github.com/MartinTschechne/ML4H2020
ECG Heartbeat Classification: A Deep Transferable Representation
https://github.com/MaxHolmberg96/GradCAM
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/Mehrab-Tanjim/enforce-reasoning
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
https://github.com/Mehrab-Tanjim/vilbert-rationalization
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
https://github.com/Mind23-2/MindCode-140
LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model
https://github.com/Mind23-2/MindCode-156
LXMERT: Learning Cross-Modality Encoder Representations from Transformers
https://github.com/MindCode-4/code-1/tree/main/tvlt
https://github.com/MindCode-4/code-1/tree/main/vilt
https://github.com/MindCode-4/code-1/tree/main/vision_text_dual_encoder
Distilled Dual-Encoder Model for Vision-Language Understanding
https://github.com/MindCode-4/code-1/tree/main/visual_bert
VisualBERT: A Simple and Performant Baseline for Vision and Language
https://github.com/MindCode-4/code-11/tree/main/dice-loss-for-data-imbalanced-nlp-tasks
https://github.com/MindCode-4/code-13/tree/main/stochastic-attention-head-removal-a-simple
Stochastic Answer Networks for Machine Reading Comprehension
https://github.com/MindCode-4/code-14/tree/main/CAJ
Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning
https://github.com/MindCode-4/code-2/tree/main/pix2struct
Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding
https://github.com/MindCode-4/code-3/tree/main/donut
https://github.com/MindCode-4/code-3/tree/main/florence2
https://github.com/MindCode-4/code-4/tree/main/qwen2_moe
Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
https://github.com/MindCode-4/code-4/tree/main/qwen2_vl
Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
https://github.com/MindCode-4/code-5/tree/main/tvlt
https://github.com/MindCode-4/code-5/tree/main/udop
Unifying Vision, Text, and Layout for Universal Document Processing
https://github.com/MindCode-4/code-5/tree/main/vision_text_dual_encoder
Distilled Dual-Encoder Model for Vision-Language Understanding
https://github.com/MindCode-4/code-5/tree/main/visual_bert
VisualBERT: A Simple and Performant Baseline for Vision and Language
https://github.com/MindCode-4/code-5/tree/main/xmod
Xmodel-VLM: A Simple Baseline for Multimodal Vision Language Model
https://github.com/MindCode-4/code-6/tree/main/drop-an-octave-reducing-spatial
https://github.com/MindCode-4/code-8/tree/main/perceptual-score
Perceptual Score: What Data Modalities Does Your Model Perceive?
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.