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 1301–1325 of 2167 papers

TitleStatusHype
Can you even tell left from right? Presenting a new challenge for VQA—0
CAPO: Reinforcing Consistent Reasoning in Medical Decision-Making—0
CAPTION: Correction by Analyses, POS-Tagging and Interpretation of Objects using only Nouns—0
Capturing Co-existing Distortions in User-Generated Content for No-reference Video Quality Assessment—0
CapWAP: Captioning with a Purpose—0
CapWAP: Image Captioning with a Purpose—0
Categorizing Concepts With Basic Level for Vision-to-Language—0
Causal-CoG: A Causal-Effect Look at Context Generation for Boosting Multi-modal Language Models—0
Causal Reasoning through Two Layers of Cognition for Improving Generalization in Visual Question Answering—0
CAVL: Learning Contrastive and Adaptive Representations of Vision and Language—0
CEGI: Measuring the trade-off between efficiency and carbon emissions for SLMs and VLMs—0
Certainly Uncertain: A Benchmark and Metric for Multimodal Epistemic and Aleatoric Awareness—0
Chain of Reasoning for Visual Question Answering—0
Characterizing Datasets for Social Visual Question Answering, and the New TinySocial Dataset—0
Characterizing Misclassifications of Deep NLP Models—0
Charting the Future: Using Chart Question-Answering for Scalable Evaluation of LLM-Driven Data Visualizations—0
ChatBEV: A Visual Language Model that Understands BEV Maps—0
ChatReID: Open-ended Interactive Person Retrieval via Hierarchical Progressive Tuning for Vision Language Models—0
ChitroJera: A Regionally Relevant Visual Question Answering Dataset for Bangla—0
Chop Chop BERT: Visual Question Answering by Chopping VisualBERT's Heads—0
CIC: A Framework for Culturally-Aware Image Captioning—0
CL-CrossVQA: A Continual Learning Benchmark for Cross-Domain Visual Question Answering—0
CLEVR-POC: Reasoning-Intensive Visual Question Answering in Partially Observable Environments—0
CLiF-VQA: Enhancing Video Quality Assessment by Incorporating High-Level Semantic Information related to Human Feelings—0
CLIP Models are Few-shot Learners: Empirical Studies on VQA and Visual Entailment—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
9InternVL-CAccuracy81.2—Unverified
10LyricsAccuracy81.2—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