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 1626–1650 of 2167 papers

TitleStatusHype
iVQA: Inverse Visual Question Answering—0
Jaeger: A Concatenation-Based Multi-Transformer VQA Model—0
Joint Image Captioning and Question Answering—0
Joint learning of object graph and relation graph for visual question answering—0
Jointly Learning Truth-Conditional Denotations and Groundings using Parallel Attention—0
JTD-UAV: MLLM-Enhanced Joint Tracking and Description Framework for Anti-UAV Systems—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
KAT: A Knowledge Augmented Transformer for Vision-and-Language—0
Kernel Pooling for Convolutional Neural Networks—0
Generating and Evaluating Explanations of Attended and Error-Inducing Input Regions for VQA Models—0
Knowing Where to Look? Analysis on Attention of Visual Question Answering System—0
KnowIT VQA: Answering Knowledge-Based Questions about Videos—0
Knowledge Acquisition for Visual Question Answering via Iterative Querying—0
Knowledge-Based Counterfactual Queries for Visual Question Answering—0
Knowledge-Based Visual Question Answering in Videos—0
Knowledge Condensation and Reasoning for Knowledge-based VQA—0
Knowledge Detection by Relevant Question and Image Attributes in Visual Question Answering—0
KNVQA: A Benchmark for evaluation knowledge-based VQA—0
KRISP: Integrating Implicit and Symbolic Knowledge for Open-Domain Knowledge-Based VQA—0
KVL-BERT: Knowledge Enhanced Visual-and-Linguistic BERT for Visual Commonsense Reasoning—0
KVQA: Knowledge-Aware Visual Question Answering—0
Language bias in Visual Question Answering: A Survey and Taxonomy—0
Language Features Matter: Effective Language Representations for Vision-Language Tasks—0
Language Models are General-Purpose Interfaces—0
LAPDoc: Layout-Aware Prompting for Documents—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