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 1026–1050 of 2167 papers

TitleStatusHype
From Wrong To Right: A Recursive Approach Towards Vision-Language Explanation—0
MF2-MVQA: A Multi-stage Feature Fusion method for Medical Visual Question Answering—0
CRIC: A VQA Dataset for Compositional Reasoning on Vision and Commonsense—0
Abduction of Domain Relationships from Data for VQA—0
From Strings to Things: Knowledge-Enabled VQA Model That Can Read and Reason—0
MGA-VQA: Multi-Granularity Alignment for Visual Question Answering—0
From Shallow to Deep: Compositional Reasoning over Graphs for Visual Question Answering—0
From Pixels to Objects: Cubic Visual Attention for Visual Question Answering—0
CL-MoE: Enhancing Multimodal Large Language Model with Dual Momentum Mixture-of-Experts for Continual Visual Question Answering—0
From Pixels to Graphs: using Scene and Knowledge Graphs for HD-EPIC VQA Challenge—0
From Known to the Unknown: Transferring Knowledge to Answer Questions about Novel Visual and Semantic Concepts—0
Does CLIP Benefit Visual Question Answering in the Medical Domain as Much as it Does in the General Domain?—0
Memory-Augmented Multimodal LLMs for Surgical VQA via Self-Contained Inquiry—0
From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities—0
CLIP-UP: CLIP-Based Unanswerable Problem Detection for Visual Question Answering—0
CLIP-TD: CLIP Targeted Distillation for Vision-Language Tasks—0
From Images to Textual Prompts: Zero-Shot Visual Question Answering With Frozen Large Language Models—0
From Easy to Hard: Learning Language-guided Curriculum for Visual Question Answering on Remote Sensing Data—0
Memory Augmented Neural Networks for Natural Language Processing—0
MGFFD-VLM: Multi-Granularity Prompt Learning for Face Forgery Detection with VLM—0
Free Form Medical Visual Question Answering in Radiology—0
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
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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