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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/showlab/mist

공식 코드

MIST: Multi-modal Iterative Spatial-Temporal Transformer for Long-form Video Question Answering

https://github.com/shrivatsagrawal/are-llms-the-master-of-all-trades-exploring-domain-agnostic-reasoning-skills-of-llms

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Are LLMs the Master of All Trades? : Exploring Domain-Agnostic Reasoning Skills of LLMs

https://github.com/shtuplus/iccc_cvpr2024

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Learning by Correction: Efficient Tuning Task for Zero-Shot Generative Vision-Language Reasoning

https://github.com/shuvenduroy/multimodal

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Benchmarking Vision-Language Contrastive Methods for Medical Representation Learning

https://github.com/siddk/vqa-outliers

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Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering

https://github.com/sidgan/whats_in_a_question

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What's in a Question: Using Visual Questions as a Form of Supervision

https://github.com/simula/Kvasir-VQA

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Kvasir-VQA: A Text-Image Pair GI Tract Dataset

https://github.com/sivandoveh/iploc

공식 코드

Teaching VLMs to Localize Specific Objects from In-context Examples

https://github.com/sjoshi804/mm-gen

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MM-GEN: Enhancing Task Performance Through Targeted Multimodal Data Curation

https://github.com/sjtuytc/Neurips21-ProTo-Program-guided-Transformers-for-Program-guided-Tasks

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ProTo: Program-Guided Transformer for Program-Guided Tasks

https://github.com/skywalkerzhang/Defeasible_Visual_Entailment

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Defeasible Visual Entailment: Benchmark, Evaluator, and Reward-Driven Optimization

https://github.com/sled-group/COMFORT

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Do Vision-Language Models Represent Space and How? Evaluating Spatial Frame of Reference Under Ambiguities

https://github.com/sled-group/moh

공식 코드

Multi-Object Hallucination in Vision-Language Models

https://github.com/smileformylove/smartfreeedit

공식 코드

SmartFreeEdit: Mask-Free Spatial-Aware Image Editing with Complex Instruction Understanding

https://github.com/snap-stanford/med-flamingo

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Med-Flamingo: a Multimodal Medical Few-shot Learner

https://github.com/snova-jonathanl/hudocvqa

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Synthetic Document Question Answering in Hungarian

https://github.com/social-ai-studio/pro-cap

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Pro-Cap: Leveraging a Frozen Vision-Language Model for Hateful Meme Detection

https://github.com/sogou/SMRCToolkit

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Sogou Machine Reading Comprehension Toolkit

https://github.com/sohojoe/clip_visual-spatial-reasoning

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Visual Spatial Reasoning

https://github.com/sonlam1102/vicoqa-cmc

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Conversational Machine Reading Comprehension for Vietnamese Healthcare Texts

https://github.com/sonlam1102/vimmrc2

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A Multiple Choices Reading Comprehension Corpus for Vietnamese Language Education

https://github.com/sonlam1102/vlogqa

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VlogQA: Task, Dataset, and Baseline Models for Vietnamese Spoken-Based Machine Reading Comprehension

https://github.com/sonqt/single-sentence-reader

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Single-Sentence Reader: A Novel Approach for Addressing Answer Position Bias

https://github.com/sootung/askchart

공식 코드

AskChart: Universal Chart Understanding through Textual Enhancement

https://github.com/souradipp76/mm-poe

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MM-PoE: Multiple Choice Reasoning via. Process of Elimination using Multi-Modal Models

https://github.com/sourjyadip/ervqa-data

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ERVQA: A Dataset to Benchmark the Readiness of Large Vision Language Models in Hospital Environments

https://github.com/spearsheep/APT-Architectural-Planning-LLM-Agent

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APT: Architectural Planning and Text-to-Blueprint Construction Using Large Language Models for Open-World Agents

https://github.com/standardmodelbio/llama3-med

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Advancing High Resolution Vision-Language Models in Biomedicine

https://github.com/stanfordnlp/mac-network

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Compositional Attention Networks for Machine Reasoning

https://github.com/stare-bench/stare

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Unfolding Spatial Cognition: Evaluating Multimodal Models on Visual Simulations

https://github.com/starreeze/efuf

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EFUF: Efficient Fine-grained Unlearning Framework for Mitigating Hallucinations in Multimodal Large Language Models

https://github.com/staymylove/3DMIT

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3DMIT: 3D Multi-modal Instruction Tuning for Scene Understanding

https://github.com/steven-ccq/visualreasoner

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From the Least to the Most: Building a Plug-and-Play Visual Reasoner via Data Synthesis

https://github.com/stogiannidis/srbench

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Mind the Gap: Benchmarking Spatial Reasoning in Vision-Language Models

https://github.com/subho406/OmniNet

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OmniNet: A unified architecture for multi-modal multi-task learning

https://github.com/sunanhe/meddr

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GSCo: Towards Generalizable AI in Medicine via Generalist-Specialist Collaboration

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