Papers with Code 아카이브

Visual Question Answering (VQA)

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하위 태스크: 9
평가표: 76
보존된 평가 기록: 725

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/ShannonAI/mrc-for-flat-nested-ner

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A Unified MRC Framework for Named Entity Recognition

https://github.com/SharkyBamboozle/iVISPAR

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iVISPAR -- An Interactive Visual-Spatial Reasoning Benchmark for VLMs

https://github.com/SinghJasdeep/Attention-on-Attention-for-VQA

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Attention on Attention: Architectures for Visual Question Answering (VQA)

https://github.com/SkelterLabsInc/JaQuAD

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JaQuAD: Japanese Question Answering Dataset for Machine Reading Comprehension

https://github.com/SkiddieAhn/Paper-AnyAnomaly

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AnyAnomaly: Zero-Shot Customizable Video Anomaly Detection with LVLM

https://github.com/SparkJiao/Self-Training-MRC

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A Self-Training Method for Machine Reading Comprehension with Soft Evidence Extraction

https://github.com/SparkJiao/retrieval-based-mrc-pretraining

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REPT: Bridging Language Models and Machine Reading Comprehension via Retrieval-Based Pre-training

https://github.com/SpencerWhitehead/novelvqa

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Separating Skills and Concepts for Novel Visual Question Answering

https://github.com/SpencerWhitehead/vqap2

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Learning from Lexical Perturbations for Consistent Visual Question Answering

https://github.com/Spico197/Mirror

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Mirror: A Universal Framework for Various Information Extraction Tasks

https://github.com/SunJiamei/LRP-imagecaptioning-pytorch

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Explain and Improve: LRP-Inference Fine-Tuning for Image Captioning Models

https://github.com/SuperSupermoon/MedViLL

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Multi-modal Understanding and Generation for Medical Images and Text via Vision-Language Pre-Training

https://github.com/THU-BPM/LLMArena

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LLMArena: Assessing Capabilities of Large Language Models in Dynamic Multi-Agent Environments

https://github.com/THU-KEG/MRCEval

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MRCEval: A Comprehensive, Challenging and Accessible Machine Reading Comprehension Benchmark

https://github.com/THUDM/CogAgent

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CogAgent: A Visual Language Model for GUI Agents

https://github.com/THUNLP-MT/ActiView

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ActiView: Evaluating Active Perception Ability for Multimodal Large Language Models

https://github.com/THUNLP-MT/EscapeCraft

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How Do Multimodal Large Language Models Handle Complex Multimodal Reasoning? Placing Them in An Extensible Escape Game

https://github.com/TJURLLAB-EAI/MFE-ETP

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MFE-ETP: A Comprehensive Evaluation Benchmark for Multi-modal Foundation Models on Embodied Task Planning

https://github.com/TXH-mercury/VALOR

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VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset

https://github.com/TencentARC/ST-LLM

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ST-LLM: Large Language Models Are Effective Temporal Learners

https://github.com/Theo-Jaunet/VisQA

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VisQA: X-raying Vision and Language Reasoning in Transformers

https://github.com/TongZhangTHU/sgr

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A Universal Semantic-Geometric Representation for Robotic Manipulation

https://github.com/TonyLianLong/LLM-groundedDiffusion

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

https://github.com/Tzoulio/Large_Models_Dialogue_for_Active_Perception

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Large Models in Dialogue for Active Perception and Anomaly Detection

https://github.com/UCF-CRCV/ImplicitQA

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ImplicitQA: Going beyond frames towards Implicit Video Reasoning

https://github.com/UCSC-VLAA/MedTrinity-25M

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MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for Medicine

https://github.com/UKPLab/coref-reasoning-in-qa

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Coreference Reasoning in Machine Reading Comprehension

https://github.com/Ucas-HaoranWei/Vary

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Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models

https://github.com/VARGPT-family/VARGPT

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VARGPT: Unified Understanding and Generation in a Visual Autoregressive Multimodal Large Language Model

https://github.com/VITA-Group/VLM-3R

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VLM-3R: Vision-Language Models Augmented with Instruction-Aligned 3D Reconstruction

https://github.com/Vanessik/HAAR

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HAAR: Text-Conditioned Generative Model of 3D Strand-based Human Hairstyles

https://github.com/VarnithChordia/Multimodal_Classification_Co_Attention

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Large Scale Multimodal Classification Using an Ensemble of Transformer Models and Co-Attention

https://github.com/VirajBagal/MMBERT

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MMBERT: Multimodal BERT Pretraining for Improved Medical VQA

https://github.com/Vision-CAIR/MiniGPT4-video

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MiniGPT4-Video: Advancing Multimodal LLMs for Video Understanding with Interleaved Visual-Textual Tokens

https://github.com/Vision-CAIR/RelTransformer

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RelTransformer: A Transformer-Based Long-Tail Visual Relationship Recognition

https://github.com/VisionLearningGroup/SANE

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Why do These Match? Explaining the Behavior of Image Similarity Models

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