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홈 뉴스 최신 연구 논문 튜토리얼 데이터셋 백과사전 SOTA LLM 모델 GPU 랭킹 컨퍼런스

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Visual Question Answering On A Okvqa

평가 지표

DA VQA Score

MC Accuracy

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	DA VQA Score	MC Accuracy	Paper Title	Repository
ViLBERT - OK-VQA	9.2	34.1	ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
GPV-2	40.7	53.7	Webly Supervised Concept Expansion for General Purpose Vision Models	-
PromptCap	59.6	73.2	PromptCap: Prompt-Guided Task-Aware Image Captioning
A Simple Baseline for KB-VQA	57.5	-	A Simple Baseline for Knowledge-Based Visual Question Answering	-
VLC-BERT	38.05	-	VLC-BERT: Visual Question Answering with Contextualized Commonsense Knowledge
PaLI-X-VPD	68.2	80.4	Visual Program Distillation: Distilling Tools and Programmatic Reasoning into Vision-Language Models	-
LXMERT	25.9	41.6	LXMERT: Learning Cross-Modality Encoder Representations from Transformers
KRISP	42.2	42.2	KRISP: Integrating Implicit and Symbolic Knowledge for Open-Domain Knowledge-Based VQA	-
Prophet	58.5	75.1	Prophet: Prompting Large Language Models with Complementary Answer Heuristics for Knowledge-based Visual Question Answering
MC-CoT	-	71	Boosting the Power of Small Multimodal Reasoning Models to Match Larger Models with Self-Consistency Training
Pythia	21.9	40.1	Pythia v0.1: the Winning Entry to the VQA Challenge 2018
SMoLA-PaLI-X Specialist Model	70.55	83.75	Omni-SMoLA: Boosting Generalist Multimodal Models with Soft Mixture of Low-rank Experts	-
ViLBERT	25.9	41.5	ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
ViLBERT - VQA	12.0	42.1	ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
HYDRA	-	56.35	HYDRA: A Hyper Agent for Dynamic Compositional Visual Reasoning

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Visual Question Answering On A Okvqa | SOTA | HyperAI초신경