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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1601–1625 of 2167 papers

TitleStatusHype
Chop Chop BERT: Visual Question Answering by Chopping VisualBERT's Heads—0
Optimal training of variational quantum algorithms without barren plateausCode0
Document Collection Visual Question Answering—0
InfographicVQA—0
Playing Lottery Tickets with Vision and Language—0
VGNMN: Video-grounded Neural Module Network to Video-Grounded Language Tasks—0
Cross-Modal Retrieval Augmentation for Multi-Modal Classification—0
Jointly Learning Truth-Conditional Denotations and Groundings using Parallel Attention—0
Dealing with Missing Modalities in the Visual Question Answer-Difference Prediction Task through Knowledge Distillation—0
Neuro-Symbolic VQA: A review from the perspective of AGI desiderata—0
CLEVR_HYP: A Challenge Dataset and Baselines for Visual Question Answering with Hypothetical Actions over ImagesCode0
How Transferable are Reasoning Patterns in VQA?—0
Multimodal Continuous Visual Attention Mechanisms—0
Compressing Visual-linguistic Model via Knowledge Distillation—0
`Just because you are right, doesn't mean I am wrong': Overcoming a bottleneck in development and evaluation of Open-Ended VQA tasks—0
UC2: Universal Cross-lingual Cross-modal Vision-and-Language Pre-training—0
Analysis on Image Set Visual Question Answering—0
Domain-robust VQA with diverse datasets and methods but no target labels—0
'Just because you are right, doesn't mean I am wrong': Overcoming a Bottleneck in the Development and Evaluation of Open-Ended Visual Question Answering (VQA) TasksCode0
Generating and Evaluating Explanations of Attended and Error-Inducing Input Regions for VQA Models—0
Visual Grounding Strategies for Text-Only Natural Language Processing—0
How to Design Sample and Computationally Efficient VQA Models—0
A Comprehensive Survey of Scene Graphs: Generation and Application—0
Automatic Generation of Contrast Sets from Scene Graphs: Probing the Compositional Consistency of GQACode0
VMAF And Variants: Towards A Unified VQA—0
Show:102550
← PrevPage 65 of 87Next →
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