Papers with Code 아카이브

Visual Question Answering

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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하위 태스크

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평가표

30

보존된 평가 기록

334

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/lauradhatt/Interesting-Reads

Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering

https://github.com/lavender105/rsgpt

Vision-Language Models in Remote Sensing: Current Progress and Future Trends

https://github.com/leaplabthu/pseudo-q

Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering

https://github.com/lflage/openfactscore

GPT-4 Technical Report

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/lichengunc/pretrain-vl-data

UNITER: UNiversal Image-TExt Representation Learning

https://github.com/lienchibao1998/new

Learning Visual Question Answering by Bootstrapping Hard Attention

https://github.com/lighterswang/biomedclip-lora

BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs

https://github.com/lil-lab/ciff

Touchdown: Natural Language Navigation and Spatial Reasoning in Visual Street Environments

https://github.com/linzhiqiu/clip-flant5

BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models

https://github.com/liuqiang3/faster_rcnn

Spatial Memory for Context Reasoning in Object Detection

https://github.com/liuqihan/Image-Caption

What value do explicit high level concepts have in vision to language problems?

https://github.com/llava-annonymous/llava

Visual Instruction Tuning

https://github.com/lllyasviel/omost

LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models

https://github.com/llmbev/talk2bev

Talk2BEV: Language-enhanced Bird's-eye View Maps for Autonomous Driving

https://github.com/longbai1006/surgical-vqla

Surgical-VQA: Visual Question Answering in Surgical Scenes using Transformer

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

An Improved Attention for Visual Question Answering

https://github.com/lucidrains/CoCa-pytorch

CoCa: Contrastive Captioners are Image-Text Foundation Models

https://github.com/lucidrains/flamingo-pytorch

Flamingo: a Visual Language Model for Few-Shot Learning

https://github.com/lucidrains/mammut-pytorch

MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks

https://github.com/luomancs/remuq

WebQA: Multihop and Multimodal QA

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/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/mappy/tf-faster-rcnn

Spatial Memory for Context Reasoning in Object Detection

https://github.com/marioskatsak/mapert

Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering

https://github.com/mathworks/Grad-CAM-for-AlexNet-to-explain-the-reason-of-classification

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/matwilso/relation-networks

A simple neural network module for relational reasoning

https://github.com/mazm13/Image-to-Tree.pytorch

Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering

https://github.com/mbzuai-oryx/unimed-clip

BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs

https://github.com/mchandak29/IQ-VQA

IQ-VQA: Intelligent Visual Question Answering

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.