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 801850 of 2167 papers

TitleStatusHype
Interpretable Visual Question Answering via Reasoning Supervision0
S3C: Semi-Supervised VQA Natural Language Explanation via Self-Critical Learning0
Can I Trust Your Answer? Visually Grounded Video Question AnsweringCode1
Distraction-free Embeddings for Robust VQA0
Separate and Locate: Rethink the Text in Text-based Visual Question AnsweringCode0
Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and BeyondCode5
VQA Therapy: Exploring Answer Differences by Visually Grounding AnswersCode0
BLIVA: A Simple Multimodal LLM for Better Handling of Text-Rich Visual QuestionsCode2
VL-PET: Vision-and-Language Parameter-Efficient Tuning via Granularity ControlCode1
Open-vocabulary Video Question Answering: A New Benchmark for Evaluating the Generalizability of Video Question Answering ModelsCode1
Uni-NLX: Unifying Textual Explanations for Vision and Vision-Language TasksCode1
Pro-Cap: Leveraging a Frozen Vision-Language Model for Hateful Meme DetectionCode1
TeCH: Text-guided Reconstruction of Lifelike Clothed HumansCode2
UGC Quality Assessment: Exploring the Impact of Saliency in Deep Feature-Based Quality AssessmentCode0
Detecting and Preventing Hallucinations in Large Vision Language ModelsCode1
StableVQA: A Deep No-Reference Quality Assessment Model for Video StabilityCode1
SciGraphQA: A Large-Scale Synthetic Multi-Turn Question-Answering Dataset for Scientific GraphsCode1
OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language ModelsCode4
Ada-DQA: Adaptive Diverse Quality-aware Feature Acquisition for Video Quality Assessment0
Making the V in Text-VQA Matter0
Workshop on Document Intelligence Understanding0
Capturing Co-existing Distortions in User-Generated Content for No-reference Video Quality Assessment0
Bridging the Gap: Exploring the Capabilities of Bridge-Architectures for Complex Visual Reasoning Tasks0
Context-VQA: Towards Context-Aware and Purposeful Visual Question AnsweringCode0
BARTPhoBEiT: Pre-trained Sequence-to-Sequence and Image Transformers Models for Vietnamese Visual Question Answering0
Med-Flamingo: a Multimodal Medical Few-shot LearnerCode2
Analysis of Video Quality Datasets via Design of Minimalistic Video Quality ModelsCode1
LOIS: Looking Out of Instance Semantics for Visual Question Answering0
Expert Knowledge-Aware Image Difference Graph Representation Learning for Difference-Aware Medical Visual Question AnsweringCode1
Robust Visual Question Answering: Datasets, Methods, and Future Challenges0
NTIRE 2023 Quality Assessment of Video Enhancement Challenge0
A reinforcement learning approach for VQA validation: an application to diabetic macular edema grading0
Explaining Autonomous Driving Actions with Visual Question AnsweringCode1
Generative Visual Question Answering0
Towards a performance analysis on pre-trained Visual Question Answering models for autonomous drivingCode0
Let's ViCE! Mimicking Human Cognitive Behavior in Image Generation Evaluation0
Rad-ReStruct: A Novel VQA Benchmark and Method for Structured Radiology ReportingCode1
Emu: Generative Pretraining in MultimodalityCode3
CAT-ViL: Co-Attention Gated Vision-Language Embedding for Visual Question Localized-Answering in Robotic SurgeryCode1
Subjective and Objective Audio-Visual Quality Assessment for User Generated ContentCode0
Divide, Evaluate, and Refine: Evaluating and Improving Text-to-Image Alignment with Iterative VQA Feedback0
Self-Adaptive Sampling for Efficient Video Question-Answering on Image--Text ModelsCode1
GPT4RoI: Instruction Tuning Large Language Model on Region-of-InterestCode2
UIT-Saviors at MEDVQA-GI 2023: Improving Multimodal Learning with Image Enhancement for Gastrointestinal Visual Question Answering0
Localized Questions in Medical Visual Question AnsweringCode1
UniFine: A Unified and Fine-grained Approach for Zero-shot Vision-Language UnderstandingCode1
DoReMi: Grounding Language Model by Detecting and Recovering from Plan-Execution Misalignment0
Lightweight Recurrent Cross-modal Encoder for Video Question AnsweringCode0
Multimodal Prompt Retrieval for Generative Visual Question AnsweringCode1
CausalVLR: A Toolbox and Benchmark for Visual-Linguistic Causal ReasoningCode3
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1humanAccuracy89.3Unverified
2DREAM+Unicoder-VL (MSRA)Accuracy76.04Unverified
3TRRNet (Ensemble)Accuracy74.03Unverified
4MIL-nbgaoAccuracy73.81Unverified
5Kakao BrainAccuracy73.33Unverified
6Coarse-to-Fine Reasoning, Single ModelAccuracy72.14Unverified
7270Accuracy70.23Unverified
8NSM ensemble (updated)Accuracy67.55Unverified
9VinVL-DPTAccuracy64.92Unverified
10VinVL+LAccuracy64.85Unverified
#ModelMetricClaimedVerifiedStatus
1PaLIAccuracy84.3Unverified
2BEiT-3Accuracy84.19Unverified
3VLMoAccuracy82.78Unverified
4ONE-PEACEAccuracy82.6Unverified
5mPLUG (Huge)Accuracy82.43Unverified
6CuMo-7BAccuracy82.2Unverified
7X2-VLM (large)Accuracy81.9Unverified
8MMUAccuracy81.26Unverified
9LyricsAccuracy81.2Unverified
10InternVL-CAccuracy81.2Unverified
#ModelMetricClaimedVerifiedStatus
1BEiT-3overall84.03Unverified
2mPLUG-Hugeoverall83.62Unverified
3ONE-PEACEoverall82.52Unverified
4X2-VLM (large)overall81.8Unverified
5VLMooverall81.3Unverified
6SimVLMoverall80.34Unverified
7X2-VLM (base)overall80.2Unverified
8VASToverall80.19Unverified
9VALORoverall78.62Unverified
10Prompt Tuningoverall78.53Unverified