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 1601–1625 of 2167 papers

TitleStatusHype
Instruction-augmented Multimodal Alignment for Image-Text and Element Matching—0
Integrating Frequency-Domain Representations with Low-Rank Adaptation in Vision-Language Models—0
Integrating Knowledge and Reasoning in Image Understanding—0
Interactive Attention AI to translate low light photos to captions for night scene understanding in women safety—0
Interactive Visual Task Learning for Robots—0
Dynamic Clue Bottlenecks: Towards Interpretable-by-Design 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
Interpretable Visual Question Answering by Reasoning on Dependency Trees—0
Interpretable Visual Question Answering by Visual Grounding from Attention Supervision Mining—0
Interpretable Visual Question Answering via Reasoning Supervision—0
Interpretable Visual Reasoning via Probabilistic Formulation under Natural Supervision—0
Inverse Visual Question Answering: A New Benchmark and VQA Diagnosis Tool—0
Inverse Visual Question Answering with Multi-Level Attentions—0
Investigating Biases in Textual Entailment Datasets—0
Investigating layer-selective transfer learning of QAOA parameters for Max-Cut problem—0
ISAAQ -- Mastering Textbook Questions with Pre-trained Transformers and Bottom-Up and Top-Down Attention—0
ISAAQ - Mastering Textbook Questions with Pre-trained Transformers and Bottom-Up and Top-Down Attention—0
Is Cognition consistent with Perception? Assessing and Mitigating Multimodal Knowledge Conflicts in Document Understanding—0
Is GPT-3 all you need for Visual Question Answering in Cultural Heritage?—0
Iterated learning for emergent systematicity in VQA—0
It Takes Two to Tango: Towards Theory of AI's Mind—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