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/luogen1996/laconvnet

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Towards Language-guided Visual Recognition via Dynamic Convolutions

https://github.com/luogen1996/llava-hr

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Feast Your Eyes: Mixture-of-Resolution Adaptation for Multimodal Large Language Models

https://github.com/luomancs/alternative_answer_set

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'Just because you are right, doesn't mean I am wrong': Overcoming a Bottleneck in the Development and Evaluation of Open-Ended Visual Question Answering (VQA) Tasks

https://github.com/luomancs/retriever_reader_for_okvqa

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Weakly-Supervised Visual-Retriever-Reader for Knowledge-based Question Answering

https://github.com/lupantech/dual-mfa-vqa

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Co-attending Free-form Regions and Detections with Multi-modal Multiplicative Feature Embedding for Visual Question Answering

https://github.com/lupantech/iconqa

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IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning

https://github.com/lupantech/rvqa

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R-VQA: Learning Visual Relation Facts with Semantic Attention for Visual Question Answering

https://github.com/lvapeab/nmt-keras

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NMT-Keras: a Very Flexible Toolkit with a Focus on Interactive NMT and Online Learning

https://github.com/lx709/vrsbench

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VRSBench: A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding

https://github.com/macehil/nesy-asp-vqa-pipeline

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A Neuro-Symbolic ASP Pipeline for Visual Question Answering

https://github.com/machine-teaching-group/tmlr2024_neurtasksyn

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Neural Task Synthesis for Visual Programming

https://github.com/mainlp/SYGAR

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Enabling Systematic Generalization in Abstract Spatial Reasoning through Meta-Learning for Compositionality

https://github.com/mair-lab/mapl

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MAPL: Parameter-Efficient Adaptation of Unimodal Pre-Trained Models for Vision-Language Few-Shot Prompting

https://github.com/manga109/mangalmm

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MangaVQA and MangaLMM: A Benchmark and Specialized Model for Multimodal Manga Understanding

https://github.com/manoja328/tallyqacode

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TallyQA: Answering Complex Counting Questions

https://github.com/marrlab/shapr_torch

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Capturing Shape Information with Multi-Scale Topological Loss Terms for 3D Reconstruction

https://github.com/marslanm/multimodality-representation-learning

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Multimodality Representation Learning: A Survey on Evolution, Pretraining and Its Applications

https://github.com/masoud-monajati/BERTHop

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BERTHop: An Effective Vision-and-Language Model for Chest X-ray Disease Diagnosis

https://github.com/mateuszmalinowski/visual_turing_test-tutorial

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Ask Your Neurons: A Deep Learning Approach to Visual Question Answering

https://github.com/matthewdm0816/bridgeqa

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Bridging the Gap between 2D and 3D Visual Question Answering: A Fusion Approach for 3D VQA

https://github.com/maum-ai/koffvqa

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KOFFVQA: An Objectively Evaluated Free-form VQA Benchmark for Large Vision-Language Models in the Korean Language

https://github.com/mayhugotong/videoinsta

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VideoINSTA: Zero-shot Long Video Understanding via Informative Spatial-Temporal Reasoning with LLMs

https://github.com/mbzuai-oryx/bimedix2

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BiMediX2: Bio-Medical EXpert LMM for Diverse Medical Modalities

https://github.com/mbzuai-oryx/geochat

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GeoChat: Grounded Large Vision-Language Model for Remote Sensing

https://github.com/mbzuai-oryx/thinkgeo

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ThinkGeo: Evaluating Tool-Augmented Agents for Remote Sensing Tasks

https://github.com/mdca-loss/mdca-calibration

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A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibration

https://github.com/merlresearch/SMART

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Are Deep Neural Networks SMARTer than Second Graders?

https://github.com/metalbubble/VQAbaseline

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Simple Baseline for Visual Question Answering

https://github.com/mi92/reverse-image-rag

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Reverse Image Retrieval Cues Parametric Memory in Multimodal LLMs

https://github.com/michi-3000/eyeclip

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EyeCLIP: A visual-language foundation model for multi-modal ophthalmic image analysis

https://github.com/microsoft/MM-REACT

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MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action

https://github.com/microsoft/PICa

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An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA

https://github.com/microsoft/UniTAB

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UniTAB: Unifying Text and Box Outputs for Grounded Vision-Language Modeling

https://github.com/microsoft/cream

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Searching the Search Space of Vision Transformer

https://github.com/microsoft/fiber

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Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone

https://github.com/microsoft/llm-data-creation

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Making Large Language Models Better Data Creators

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