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 301–325 of 2167 papers

TitleStatusHype
Document Haystacks: Vision-Language Reasoning Over Piles of 1000+ DocumentsCode0
Enhancing Instruction-Following Capability of Visual-Language Models by Reducing Image Redundancy—0
ReWind: Understanding Long Videos with Instructed Learnable Memory—0
mR^2AG: Multimodal Retrieval-Reflection-Augmented Generation for Knowledge-Based VQA—0
Benchmarking Multimodal Models for Ukrainian Language Understanding Across Academic and Cultural Domains—0
Visual Contexts Clarify Ambiguous Expressions: A Benchmark DatasetCode0
Uni-Mlip: Unified Self-supervision for Medical Vision Language Pre-training—0
Hints of Prompt: Enhancing Visual Representation for Multimodal LLMs in Autonomous Driving—0
Learning to Reason Iteratively and Parallelly for Complex Visual Reasoning Scenarios—0
LaVida Drive: Vision-Text Interaction VLM for Autonomous Driving with Token Selection, Recovery and Enhancement—0
Teaching VLMs to Localize Specific Objects from In-context ExamplesCode1
Med-2E3: A 2D-Enhanced 3D Medical Multimodal Large Language Model—0
Value-Spectrum: Quantifying Preferences of Vision-Language Models via Value Decomposition in Social Media ContextsCode0
F^3OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics—0
Understanding Multimodal LLMs: the Mechanistic Interpretability of Llava in Visual Question AnsweringCode0
Memory-Augmented Multimodal LLMs for Surgical VQA via Self-Contained Inquiry—0
A Comprehensive Survey on Visual Question Answering Datasets and Algorithms—0
Awaker2.5-VL: Stably Scaling MLLMs with Parameter-Efficient Mixture of ExpertsCode1
Visual question answering based evaluation metrics for text-to-image generation—0
Is Cognition consistent with Perception? Assessing and Mitigating Multimodal Knowledge Conflicts in Document Understanding—0
SparrowVQE: Visual Question Explanation for Course Content UnderstandingCode0
An Empirical Analysis on Spatial Reasoning Capabilities of Large Multimodal ModelsCode1
Aligned Vector Quantization for Edge-Cloud Collabrative Vision-Language Models—0
NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA—0
Select2Plan: Training-Free ICL-Based Planning through VQA and Memory Retrieval—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