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 150 of 2167 papers

TitleStatusHype
Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any ResolutionCode11
Qwen2.5-VL Technical ReportCode11
SWIFT:A Scalable lightWeight Infrastructure for Fine-TuningCode11
mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language ModelsCode7
Infinity-MM: Scaling Multimodal Performance with Large-Scale and High-Quality Instruction DataCode7
MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language ModelsCode7
GPT-4 Technical ReportCode6
Improved Baselines with Visual Instruction TuningCode6
VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMsCode5
TextMonkey: An OCR-Free Large Multimodal Model for Understanding DocumentCode5
Ovis: Structural Embedding Alignment for Multimodal Large Language ModelCode5
CogAgent: A Visual Language Model for GUI AgentsCode5
BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and GenerationCode5
VisionLLM v2: An End-to-End Generalist Multimodal Large Language Model for Hundreds of Vision-Language TasksCode5
LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init AttentionCode5
LLaMA-Adapter V2: Parameter-Efficient Visual Instruction ModelCode5
CogVLM: Visual Expert for Pretrained Language ModelsCode5
Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and BeyondCode5
OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language ModelsCode4
Otter: A Multi-Modal Model with In-Context Instruction TuningCode4
GLIPv2: Unifying Localization and Vision-Language UnderstandingCode4
mPLUG-Owl: Modularization Empowers Large Language Models with MultimodalityCode4
mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality CollaborationCode4
OmniMedVQA: A New Large-Scale Comprehensive Evaluation Benchmark for Medical LVLMCode4
Exploring the Capabilities of Large Multimodal Models on Dense TextCode4
OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual ReasoningCode4
Flamingo: a Visual Language Model for Few-Shot LearningCode4
Multi-label Cluster Discrimination for Visual Representation LearningCode4
Video-LLaVA: Learning United Visual Representation by Alignment Before ProjectionCode4
Tarsier: Recipes for Training and Evaluating Large Video Description ModelsCode4
Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional TokenizationCode4
LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision TokenCode4
SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language ModelsCode4
InternVideo: General Video Foundation Models via Generative and Discriminative LearningCode4
mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and VideoCode4
BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language ModelsCode4
Long Context Transfer from Language to VisionCode4
HuatuoGPT-Vision, Towards Injecting Medical Visual Knowledge into Multimodal LLMs at ScaleCode3
ONE-PEACE: Exploring One General Representation Model Toward Unlimited ModalitiesCode3
Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning AgentCode3
All You May Need for VQA are Image CaptionsCode3
Emu: Generative Pretraining in MultimodalityCode3
Evaluating Text-to-Visual Generation with Image-to-Text GenerationCode3
MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language ModelsCode3
MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-MakingCode3
MMSearch-R1: Incentivizing LMMs to SearchCode3
DriveLM: Driving with Graph Visual Question AnsweringCode3
Ludwig: a type-based declarative deep learning toolboxCode3
Lyra: An Efficient and Speech-Centric Framework for Omni-CognitionCode3
OCR-free Document Understanding TransformerCode3
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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