SOTAVerified

Visual Question Answering (VQA)

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

Papers

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Showing 851–900 of 2167 papers

TitleStatusHype
Improving Users' Mental Model with Attention-directed Counterfactual Edits—0
Improving Vision-and-Language Reasoning via Spatial Relations Modeling—0
Improving Visual Question Answering by Referring to Generated Paragraph Captions—0
Improving Visual Question Answering Models through Robustness Analysis and In-Context Learning with a Chain of Basic Questions—0
Aligning MAGMA by Few-Shot Learning and Finetuning—0
Learning Visual Knowledge Memory Networks for Visual Question Answering—0
Graph Neural Networks in Vision-Language Image Understanding: A Survey—0
Cycle-Consistency for Robust Visual Question Answering—0
Compositional Memory for Visual Question Answering—0
In Factuality: Efficient Integration of Relevant Facts for Visual Question Answering—0
Achieving Human Parity on Visual Question Answering—0
Graph Edit Distance Reward: Learning to Edit Scene Graph—0
A survey on VQA_Datasets and Approaches—0
A survey on knowledge-enhanced multimodal learning—0
Instance-Level Trojan Attacks on Visual Question Answering via Adversarial Learning in Neuron Activation Space—0
Graph-based Heuristic Search for Module Selection Procedure in Neural Module Network—0
Instruction-augmented Multimodal Alignment for Image-Text and Element Matching—0
GRAM: Global Reasoning for Multi-Page VQA—0
Compositional Attention Networks for Interpretability in Natural Language Question Answering—0
Integrating Frequency-Domain Representations with Low-Rank Adaptation in Vision-Language Models—0
A Causal Approach to Mitigate Modality Preference Bias in Medical Visual Question Answering—0
Learning to Recognize the Unseen Visual Predicates—0
Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision—0
Interactive Visual Task Learning for Robots—0
Leveraging Visual Question Answering to Improve Text-to-Image Synthesis—0
Davidsonian Scene Graph: Improving Reliability in Fine-grained Evaluation for Text-to-Image Generation—0
Component Analysis for Visual Question Answering Architectures—0
Aligned Image-Word Representations Improve Inductive Transfer Across Vision-Language Tasks—0
A Study on Multimodal and Interactive Explanations for Visual Question Answering—0
Interpretable Counting for Visual Question Answering—0
Interpretable Face Anti-Spoofing: Enhancing Generalization with Multimodal Large Language Models—0
Interpretable Medical Image Visual Question Answering via Multi-Modal Relationship Graph Learning—0
Interpretable Neural Computation for Real-World Compositional Visual Question Answering—0
Interpretable Visual Question Answering Referring to Outside Knowledge—0
Learning to Disambiguate by Asking Discriminative Questions—0
Interpretable Visual Question Answering by Visual Grounding from Attention Supervision Mining—0
GPT-4o System Card—0
Compact Tensor Pooling for Visual Question Answering—0
Good, Better, Best: Textual Distractors Generation for Multiple-Choice Visual Question Answering via Reinforcement Learning—0
Astrea: A MOE-based Visual Understanding Model with Progressive Alignment—0
Aligned Vector Quantization for Edge-Cloud Collabrative Vision-Language Models—0
Goal-Oriented Semantic Communication for Wireless Visual Question Answering—0
Commonsense Video Question Answering through Video-Grounded Entailment Tree Reasoning—0
Inverse Visual Question Answering with Multi-Level Attentions—0
Getting ViT in Shape: Scaling Laws for Compute-Optimal Model Design—0
ComicsPAP: understanding comic strips by picking the correct panel—0
GeoRSMLLM: A Multimodal Large Language Model for Vision-Language Tasks in Geoscience and Remote Sensing—0
Decoupled Box Proposal and Featurization with Ultrafine-Grained Semantic Labels Improve Image Captioning and Visual Question Answering—0
Geometry-Aware Video Quality Assessment for Dynamic Digital Human—0
Evaluating and Improving Interactions with Hazy Oracles—0
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All datasetsGQA Test2019VQA v2 test-devVQA v2 test-stdOK-VQAMSVD-QADocVQA testMSRVTT-QAInfographicVQAGQA test-devVizWiz 2020 VQAA-OKVQACLEVR

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1humanAccuracy89.3—Unverified
2DREAM+Unicoder-VL (MSRA)Accuracy76.04—Unverified
3TRRNet (Ensemble)Accuracy74.03—Unverified
4MIL-nbgaoAccuracy73.81—Unverified
5Kakao BrainAccuracy73.33—Unverified
6Coarse-to-Fine Reasoning, Single ModelAccuracy72.14—Unverified
7270Accuracy70.23—Unverified
8NSM ensemble (updated)Accuracy67.55—Unverified
9VinVL-DPTAccuracy64.92—Unverified
10VinVL+LAccuracy64.85—Unverified
#ModelMetricClaimedVerifiedStatus
1PaLIAccuracy84.3—Unverified
2BEiT-3Accuracy84.19—Unverified
3VLMoAccuracy82.78—Unverified
4ONE-PEACEAccuracy82.6—Unverified
5mPLUG (Huge)Accuracy82.43—Unverified
6CuMo-7BAccuracy82.2—Unverified
7X2-VLM (large)Accuracy81.9—Unverified
8MMUAccuracy81.26—Unverified
9LyricsAccuracy81.2—Unverified
10InternVL-CAccuracy81.2—Unverified
#ModelMetricClaimedVerifiedStatus
1BEiT-3overall84.03—Unverified
2mPLUG-Hugeoverall83.62—Unverified
3ONE-PEACEoverall82.52—Unverified
4X2-VLM (large)overall81.8—Unverified
5VLMooverall81.3—Unverified
6SimVLMoverall80.34—Unverified
7X2-VLM (base)overall80.2—Unverified
8VASToverall80.19—Unverified
9VALORoverall78.62—Unverified
10Prompt Tuningoverall78.53—Unverified