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

Showing 201–250 of 2167 papers

TitleStatusHype
FunQA: Towards Surprising Video ComprehensionCode1
A Dataset and Baselines for Visual Question Answering on ArtCode1
Gemini Goes to Med School: Exploring the Capabilities of Multimodal Large Language Models on Medical Challenge Problems & HallucinationsCode1
Found a Reason for me? Weakly-supervised Grounded Visual Question Answering using CapsulesCode1
From the Least to the Most: Building a Plug-and-Play Visual Reasoner via Data SynthesisCode1
Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model EvaluationCode1
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based LocalizationCode1
GraghVQA: Language-Guided Graph Neural Networks for Graph-based Visual Question AnsweringCode1
Analysis of Video Quality Datasets via Design of Minimalistic Video Quality ModelsCode1
Graph Optimal Transport for Cross-Domain AlignmentCode1
2BiVQA: Double Bi-LSTM based Video Quality Assessment of UGC VideosCode1
HAAR: Text-Conditioned Generative Model of 3D Strand-based Human HairstylesCode1
Awaker2.5-VL: Stably Scaling MLLMs with Parameter-Efficient Mixture of ExpertsCode1
Attention in Reasoning: Dataset, Analysis, and ModelingCode1
ChipQA: No-Reference Video Quality Prediction via Space-Time ChipsCode1
Hierarchical multimodal transformers for Multi-Page DocVQACode1
BadCM: Invisible Backdoor Attack Against Cross-Modal LearningCode1
How Many Unicorns Are in This Image? A Safety Evaluation Benchmark for Vision LLMsCode1
Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder TransformersCode1
GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question AnsweringCode1
Bayesian Attention ModulesCode1
Hypergraph Transformer: Weakly-supervised Multi-hop Reasoning for Knowledge-based Visual Question AnsweringCode1
Visual Grounding Methods for VQA are Working for the Wrong Reasons!Code1
A Comparison of Pre-trained Vision-and-Language Models for Multimodal Representation Learning across Medical Images and ReportsCode1
An Empirical Analysis on Spatial Reasoning Capabilities of Large Multimodal ModelsCode1
IMPACT: A Large-scale Integrated Multimodal Patent Analysis and Creation Dataset for Design PatentsCode1
Learning to Answer Visual Questions from Web VideosCode1
Attention-Based Context Aware Reasoning for Situation RecognitionCode1
FiLM: Visual Reasoning with a General Conditioning LayerCode1
InfMLLM: A Unified Framework for Visual-Language TasksCode1
Calibrating Concepts and Operations: Towards Symbolic Reasoning on Real ImagesCode1
Change Detection Meets Visual Question AnsweringCode1
An Empirical Study of End-to-End Video-Language Transformers with Masked Visual ModelingCode1
InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic TasksCode1
An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQACode1
Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual FeaturesCode1
Beyond Question-Based Biases: Assessing Multimodal Shortcut Learning in Visual Question AnsweringCode1
An Empirical Study of Multimodal Model MergingCode1
Beyond Task Performance: Evaluating and Reducing the Flaws of Large Multimodal Models with In-Context LearningCode1
An Empirical Study of Training End-to-End Vision-and-Language TransformersCode1
Knowledge-Routed Visual Question Reasoning: Challenges for Deep Representation EmbeddingCode1
Bilateral Cross-Modality Graph Matching Attention for Feature Fusion in Visual Question AnsweringCode1
FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene UnderstandingCode1
Fast Prompt Alignment for Text-to-Image GenerationCode1
Label-Descriptive Patterns and Their Application to Characterizing Classification ErrorsCode1
LaKo: Knowledge-driven Visual Question Answering via Late Knowledge-to-Text InjectionCode1
LaPA: Latent Prompt Assist Model For Medical Visual Question AnsweringCode1
Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion PerceptionCode1
Large-Scale Adversarial Training for Vision-and-Language Representation LearningCode1
A Symmetric Dual Encoding Dense Retrieval Framework for Knowledge-Intensive Visual Question AnsweringCode1
Show:102550
← PrevPage 5 of 44Next →

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
9InternVL-CAccuracy81.2—Unverified
10LyricsAccuracy81.2—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