https://github.com/lduml/blog
Stochastic Answer Networks for Machine Reading Comprehension
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/leaplabthu/pseudo-q
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/lemon234071/oc_parlai
https://github.com/lflage/openfactscore
https://github.com/lgraesser/MultimodalGame
ShapeWorld - A new test methodology for multimodal language understanding
https://github.com/lhao499/language-quantized-autoencoders
Language Quantized AutoEncoders: Towards Unsupervised Text-Image Alignment
https://github.com/libertatis/mrc-cbt
https://github.com/lichengunc/pretrain-vl-data
https://github.com/lienchibao1998/new
Learning Visual Question Answering by Bootstrapping Hard Attention
https://github.com/lighterswang/biomedclip-lora
https://github.com/likaixin2000/screenspot-pro-gui-grounding
https://github.com/lil-lab/ciff
Touchdown: Natural Language Navigation and Spatial Reasoning in Visual Street Environments
https://github.com/linzhiqiu/clip-flant5
https://github.com/liuqiang3/faster_rcnn
https://github.com/liuqihan/Image-Caption
What value do explicit high level concepts have in vision to language problems?
https://github.com/ljleeworking/4-Heartbeat-Categorization-from-ECG-Signal
ECG Heartbeat Classification: A Deep Transferable Representation
https://github.com/llava-annonymous/llava
https://github.com/llavar/llavar-2
LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding
https://github.com/lllyasviel/omost
https://github.com/llmbev/talk2bev
Talk2BEV: Language-enhanced Bird's-eye View Maps for Autonomous Driving
https://github.com/longjj/Caffe-SGDR
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
https://github.com/lpcan/micl
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
https://github.com/lucidrains/AoA-pytorch
https://github.com/lucidrains/CoCa-pytorch
CoCa: Contrastive Captioners are Image-Text Foundation Models
https://github.com/lucidrains/flamingo-pytorch
https://github.com/lucidrains/mammut-pytorch
MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks
https://github.com/ludwig-ai/ludwig
https://github.com/luisrui/Modality-Interference-in-MLLMs
Diagnosing and Mitigating Modality Interference in Multimodal Large Language Models
https://github.com/lupantech/MathVista
MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts
https://github.com/lx709/reobench
VRSBench: A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding
https://github.com/lyan62/data-curation
Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks
https://github.com/lyy1994/plm_as_kb
https://github.com/lzw-lzw/awesome-remote-sensing-vision-language-models
Vision-Language Models in Remote Sensing: Current Progress and Future Trends
https://github.com/mammoth-vl/mammoth-vl
MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale
https://github.com/manliu1225/mrc-for-flat-nested-ner
https://github.com/mappy/tf-faster-rcnn
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.