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 1051–1075 of 2167 papers

TitleStatusHype
Mitigating Dialogue Hallucination for Large Vision Language Models via Adversarial Instruction Tuning—0
Knowledge Condensation and Reasoning for Knowledge-based VQA—0
Few-Shot Image Classification and Segmentation as Visual Question Answering Using Vision-Language Models—0
UniCode: Learning a Unified Codebook for Multimodal Large Language Models—0
Adversarial Training with OCR Modality Perturbation for Scene-Text Visual Question AnsweringCode0
OmniCount: Multi-label Object Counting with Semantic-Geometric Priors—0
SnapNTell: Enhancing Entity-Centric Visual Question Answering with Retrieval Augmented Multimodal LLM—0
CLEVR-POC: Reasoning-Intensive Visual Question Answering in Partially Observable Environments—0
Enhancing Generalization in Medical Visual Question Answering Tasks via Gradient-Guided Model Perturbation—0
ArcSin: Adaptive ranged cosine Similarity injected noise for Language-Driven Visual Tasks—0
LLM-Assisted Multi-Teacher Continual Learning for Visual Question Answering in Robotic SurgeryCode0
CommVQA: Situating Visual Question Answering in Communicative ContextsCode0
A Spectrum Evaluation Benchmark for Medical Multi-Modal Large Language Models—0
PaLM2-VAdapter: Progressively Aligned Language Model Makes a Strong Vision-language Adapter—0
II-MMR: Identifying and Improving Multi-modal Multi-hop Reasoning in Visual Question AnsweringCode0
VQAttack: Transferable Adversarial Attacks on Visual Question Answering via Pre-trained Models—0
LAPDoc: Layout-Aware Prompting for Documents—0
Prompt-based Personalized Federated Learning for Medical Visual Question Answering—0
Pretraining Vision-Language Model for Difference Visual Question Answering in Longitudinal Chest X-raysCode0
Visual Question Answering Instruction: Unlocking Multimodal Large Language Model To Domain-Specific Visual Multitasks—0
CIC: A Framework for Culturally-Aware Image Captioning—0
Convincing Rationales for Visual Question Answering ReasoningCode0
Curriculum reinforcement learning for quantum architecture search under hardware errors—0
Knowledge Generation for Zero-shot Knowledge-based VQACode0
Binding Touch to Everything: Learning Unified Multimodal Tactile Representations—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