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 1551–1575 of 2167 papers

TitleStatusHype
Hierarchical Memory for Long Video QA—0
Hierarchical Modeling for Medical Visual Question Answering with Cross-Attention Fusion—0
High Frame Rate Video Quality Assessment using VMAF and Entropic Differences—0
Highly Efficient No-reference 4K Video Quality Assessment with Full-Pixel Covering Sampling and Training Strategy—0
Hints of Prompt: Enhancing Visual Representation for Multimodal LLMs in Autonomous Driving—0
HiTeA: Hierarchical Temporal-Aware Video-Language Pre-training—0
How Far Can Off-the-Shelf Multimodal Large Language Models Go in Online Episodic Memory Question Answering?—0
How good are deep models in understanding the generated images?—0
How Much Can CLIP Benefit Vision-and-Language Tasks?—0
How (not) to ensemble LVLMs for VQA—0
How to Design Sample and Computationally Efficient VQA Models—0
How to find a good image-text embedding for remote sensing visual question answering?—0
How Transferable are Reasoning Patterns in VQA?—0
How Well Can Vison-Language Models Understand Humans' Intention? An Open-ended Theory of Mind Question Evaluation Benchmark—0
HRVQA: A Visual Question Answering Benchmark for High-Resolution Aerial Images—0
Human-Adversarial Visual Question Answering—0
Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?—0
Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?—0
Hummingbird: High Fidelity Image Generation via Multimodal Context Alignment—0
HVS Revisited: A Comprehensive Video Quality Assessment Framework—0
Hyperbolic Attention Networks—0
Hyper-dimensional computing for a visual question-answering system that is trainable end-to-end—0
Hypo3D: Exploring Hypothetical Reasoning in 3D—0
ICDAR 2019 Competition on Scene Text Visual Question Answering—0
ICDAR 2021 Competition on Document VisualQuestion Answering—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