Visual Question Answering On Docvqa Test

Metrics

ANLS

Results

Performance results of various models on this benchmark

Model Name	ANLS	Paper Title	Repository
MatCha	0.742	MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering	-
GPT-4	0.884	Layout and Task Aware Instruction Prompt for Zero-shot Document Image Question Answering	-
PaLI-3	0.876	PaLI-3 Vision Language Models: Smaller, Faster, Stronger	-
Qwen-VL	0.651	Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond	-
ERNIE-Layout large	0.8486	ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding	-
DUBLIN	0.782	DUBLIN -- Document Understanding By Language-Image Network	-
Pix2Struct-base	0.721	Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding	-
DUBLIN (variable resolution)	0.803	DUBLIN -- Document Understanding By Language-Image Network	-
PaLI-3 (w/ OCR)	0.886	PaLI-3 Vision Language Models: Smaller, Faster, Stronger	-
Qwen-VL-Plus	0.9024	Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond	-
PaLI-X (Single-task FT w/ OCR)	0.868	PaLI-X: On Scaling up a Multilingual Vision and Language Model	-
PaLI-X (Single-task FT)	0.80	PaLI-X: On Scaling up a Multilingual Vision and Language Model	-
Claude + LATIN-Prompt	0.8336	Layout and Task Aware Instruction Prompt for Zero-shot Document Image Question Answering	-
TILT-Large	0.8705	Going Full-TILT Boogie on Document Understanding with Text-Image-Layout Transformer	-
BERT_LARGE_SQUAD_DOCVQA_FINETUNED_Baseline	0.665	DocVQA: A Dataset for VQA on Document Images	-
DocFormerv2-large	0.8784	DocFormerv2: Local Features for Document Understanding	-
MLCD-Embodied-7B	0.916	Multi-label Cluster Discrimination for Visual Representation Learning	-
UDOP (aux)	0.878	Unifying Vision, Text, and Layout for Universal Document Processing	-
UDOP	0.847	Unifying Vision, Text, and Layout for Universal Document Processing	-
SMoLA-PaLI-X Generalist	0.906	Omni-SMoLA: Boosting Generalist Multimodal Models with Soft Mixture of Low-rank Experts	-

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