Papers with Code 아카이브

Visual Question Answering

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

5

평가표

30

보존된 평가 기록

334

코드

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

https://github.com/facebookresearch/pythia

Bilinear Attention Networks

https://github.com/fcUalberta/UAlberta-Multimedia-Masters-Program-Interpretable-AI-Part_1_2

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

https://github.com/fcorencoret/dynamic-rn

A simple neural network module for relational reasoning

https://github.com/feifengwhu/question_attention

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

https://github.com/fitushar/3D-Grad-CAM

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

https://github.com/flagopen/flagscale

Emu3: Next-Token Prediction is All You Need

https://github.com/frgfm/torch-cam

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

https://github.com/frkl/active-learning

Active Learning for Visual Question Answering: An Empirical Study

https://github.com/fudan-zvg/spar

From Flatland to Space: Teaching Vision-Language Models to Perceive and Reason in 3D

https://github.com/fuqianya/ViLBERT-Paddle

ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks

https://github.com/fuqianya/bottom-up-attention-paddle

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

https://github.com/fuxiaoliu/mmc

Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning

https://github.com/gangasani-anusha/Short-Story_Assignment

Visual Question Answering: which investigated applications?

https://github.com/gchhablani/multilingual-vqa

How Much Can CLIP Benefit Vision-and-Language Tasks?

https://github.com/gengchenmai/se-kge

SE-KGE: A Location-Aware Knowledge Graph Embedding Model for Geographic Question Answering and Spatial Semantic Lifting

https://github.com/gicheonkang/gst-visdial

Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts

https://github.com/gitlimlab/Relation-Network-Tensorflow

A simple neural network module for relational reasoning

https://github.com/givenbysun/hyper-class-augmented-VQA

Augmenting Visual Question Answering with Semantic Frame Information in a Multitask Learning Approach

https://github.com/gngdb/relational

A simple neural network module for relational reasoning

https://github.com/google-deepmind/proactive_t2i_agents

Evaluating Text-to-Visual Generation with Image-to-Text Generation

https://github.com/google-research-datasets/RxR

Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments

https://github.com/google-research/bigbird

Long Range Arena: A Benchmark for Efficient Transformers

https://github.com/gpt4life/alpagasus

GPT-4 Technical Report

https://github.com/gqa-ood/gqa-ood

Roses Are Red, Violets Are Blue... but Should Vqa Expect Them To?

https://github.com/gregor-ge/mblip

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

https://github.com/gshashank84/CheXNet

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

https://github.com/guyd1995/lra-benchmark

Long Range Arena: A Benchmark for Efficient Transformers

https://github.com/guyd1995/lra-benchmarks

Long Range Arena: A Benchmark for Efficient Transformers

https://github.com/h-zhao1997/cobra

Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning

https://github.com/hadi-hosseini/t2i-fineeval

T2I-FineEval: Fine-Grained Compositional Metric for Text-to-Image Evaluation

https://github.com/hanhpt23/silvar

SilVar: Speech Driven Multimodal Model for Reasoning Visual Question Answering and Object Localization

https://github.com/happen2me/cross-gnn

Flamingo: a Visual Language Model for Few-Shot Learning

https://github.com/hengyuan-hu/bottom-up-attention-vqa

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

https://github.com/hieunghia-pat/UIT-MCAN

Deep Modular Co-Attention Networks for Visual Question Answering

https://github.com/hj963434688/Faster_rcnn

Spatial Memory for Context Reasoning in Object Detection

https://github.com/hjjpku/dynamic_graph_caption

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

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