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 226–250 of 2167 papers

TitleStatusHype
Commonsense Video Question Answering through Video-Grounded Entailment Tree Reasoning—0
Enhancing Financial VQA in Vision Language Models using Intermediate Structured Representations—0
Visual question answering: from early developments to recent advances -- a survey—0
LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision TokenCode4
ReDiT: Re‑evaluating large visual question answering model confidence by defining input scenario Difficulty and applying Temperature mappingCode0
Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model EvaluationCode1
Multilevel Semantic-Aware Model for AI-Generated Video Quality Assessment—0
Generalizing from SIMPLE to HARD Visual Reasoning: Can We Mitigate Modality Imbalance in VLMs?Code1
Accounting for Focus Ambiguity in Visual Questions—0
Guiding Medical Vision-Language Models with Explicit Visual Prompts: Framework Design and Comprehensive Exploration of Prompt Variations—0
Interpretable Face Anti-Spoofing: Enhancing Generalization with Multimodal Large Language Models—0
MoColl: Agent-Based Specific and General Model Collaboration for Image Captioning—0
CLIP-UP: CLIP-Based Unanswerable Problem Detection for Visual Question Answering—0
V^2Dial: Unification of Video and Visual Dialog via Multimodal Experts—0
F^3OCUS - Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics—0
JTD-UAV: MLLM-Enhanced Joint Tracking and Description Framework for Anti-UAV Systems—0
Alignment, Mining and Fusion: Representation Alignment with Hard Negative Mining and Selective Knowledge Fusion for Medical Visual Question Answering—0
Notes-guided MLLM Reasoning: Enhancing MLLM with Knowledge and Visual Notes for Visual Question AnsweringCode1
Probing Visual Language Priors in VLMs—0
Social-LLaVA: Enhancing Robot Navigation through Human-Language Reasoning in Social Spaces—0
Investigating layer-selective transfer learning of QAOA parameters for Max-Cut problem—0
Enhanced Multimodal RAG-LLM for Accurate Visual Question Answering—0
ESVQA: Perceptual Quality Assessment of Egocentric Spatial Videos—0
HALLUCINOGEN: A Benchmark for Evaluating Object Hallucination in Large Visual-Language ModelsCode0
Not all Views are Created Equal: Analyzing Viewpoint Instabilities in Vision Foundation Models—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