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
CLIP Models are Few-shot Learners: Empirical Studies on VQA and Visual Entailment—0
A Short Survey of Systematic Generalization—0
FOVQA: Blind Foveated Video Quality Assessment—0
A Shared Task on Multimodal Machine Translation and Crosslingual Image Description—0
Mitigating Dialogue Hallucination for Large Vision Language Models via Adversarial Instruction Tuning—0
Foundational Model for Electron Micrograph Analysis: Instruction-Tuning Small-Scale Language-and-Vision Assistant for Enterprise Adoption—0
Fooling Vision and Language Models Despite Localization and Attention Mechanism—0
CLiF-VQA: Enhancing Video Quality Assessment by Incorporating High-Level Semantic Information related to Human Feelings—0
Focus on What Matters: Enhancing Medical Vision-Language Models with Automatic Attention Alignment Tuning—0
FOCUS: Internal MLLM Representations for Efficient Fine-Grained Visual Question Answering—0
CLEVR-POC: Reasoning-Intensive Visual Question Answering in Partially Observable Environments—0
Focused Evaluation for Image Description with Binary Forced-Choice Tasks—0
ASCD: Attention-Steerable Contrastive Decoding for Reducing Hallucination in MLLM—0
FMBench: Benchmarking Fairness in Multimodal Large Language Models on Medical Tasks—0
A Spectrum Evaluation Benchmark for Medical Multi-Modal Large Language Models—0
Misleading Failures of Partial-input Baselines—0
AI2D-RST: A multimodal corpus of 1000 primary school science diagrams—0
FlexCap: Describe Anything in Images in Controllable Detail—0
FineVQ: Fine-Grained User Generated Content Video Quality Assessment—0
Fine-tuning vs From Scratch: Do Vision & Language Models Have Similar Capabilities on Out-of-Distribution Visual Question Answering?—0
Correlation Information Bottleneck: Towards Adapting Pretrained Multimodal Models for Robust Visual Question Answering—0
Fine-Grained Retrieval-Augmented Generation for Visual Question Answering—0
Are You Talking to Me? Reasoned Visual Dialog Generation through Adversarial Learning—0
CL-CrossVQA: A Continual Learning Benchmark for Cross-Domain Visual Question Answering—0
Find The Gap: Knowledge Base Reasoning For Visual Question Answering—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