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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 626–650 of 2167 papers

TitleStatusHype
DiffVQA: Video Quality Assessment Using Diffusion Feature Extractor—0
Advancing Surgical VQA with Scene Graph Knowledge—0
Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?—0
Hyperbolic Attention Networks—0
How to find a good image-text embedding for remote sensing visual question answering?—0
How Transferable are Reasoning Patterns in VQA?—0
Advancing Multimodal Medical Capabilities of Gemini—0
How to Design Sample and Computationally Efficient VQA Models—0
How Well Can Vison-Language Models Understand Humans' Intention? An Open-ended Theory of Mind Question Evaluation Benchmark—0
Differentiable End-to-End Program Executor for Sample and Computationally Efficient VQA—0
DIEM: Decomposition-Integration Enhancing Multimodal Insights—0
Beyond Human Vision: The Role of Large Vision Language Models in Microscope Image Analysis—0
Beyond Captioning: Task-Specific Prompting for Improved VLM Performance in Mathematical Reasoning—0
How (not) to ensemble LVLMs for VQA—0
HRVQA: A Visual Question Answering Benchmark for High-Resolution Aerial Images—0
Advancing Large Multi-modal Models with Explicit Chain-of-Reasoning and Visual Question Generation—0
Detecting Multimodal Situations with Insufficient Context and Abstaining from Baseless Predictions—0
Detect, Describe, Discriminate: Moving Beyond VQA for MLLM Evaluation—0
BESTMVQA: A Benchmark Evaluation System for Medical Visual Question Answering—0
Detect2Interact: Localizing Object Key Field in Visual Question Answering (VQA) with LLMs—0
An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation—0
How good are deep models in understanding the generated images?—0
How Much Can CLIP Benefit Vision-and-Language Tasks?—0
DePlot: One-shot visual language reasoning by plot-to-table translation—0
What BERT Sees: Cross-Modal Transfer for Visual Question Generation—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