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Found 12 papers, 12 papers with code
OmniDocBench: Benchmarking Diverse PDF Document Parsing with Comprehensive Annotations
Document content extraction is a critical task in computer vision, underpinning the data needs of large language models (LLMs) and retrieval-augmented generation (RAG) systems.
OCR Hinders RAG: Evaluating the Cascading Impact of OCR on Retrieval-Augmented Generation
In this paper, we introduce OHRBench, the first benchmark for understanding the cascading impact of OCR on RAG systems.
Optical Character Recognition
Optical Character Recognition (OCR)
+2
MinerU: An Open-Source Solution for Precise Document Content Extraction
Document content analysis has been a crucial research area in computer vision.
Image Over Text: Transforming Formula Recognition Evaluation with Character Detection Matching
Such a spatially-aware and character-matching method offers a more accurate and equitable evaluation compared with previous BLEU and Edit Distance metrics that rely solely on text-based character matching.
InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output
This long-context capability allows IXC-2. 5 to excel in tasks requiring extensive input and output contexts.
Ranked #4 on
Video Question Answering
on TVBench
DSDL: Data Set Description Language for Bridging Modalities and Tasks in AI Data
In the era of artificial intelligence, the diversity of data modalities and annotation formats often renders data unusable directly, requiring understanding and format conversion before it can be used by researchers or developers with different needs.
InternLM-XComposer2-4KHD: A Pioneering Large Vision-Language Model Handling Resolutions from 336 Pixels to 4K HD
The Large Vision-Language Model (LVLM) field has seen significant advancements, yet its progression has been hindered by challenges in comprehending fine-grained visual content due to limited resolution.
Ranked #47 on
Visual Question Answering
on MM-Vet
InternLM2 Technical Report
The evolution of Large Language Models (LLMs) like ChatGPT and GPT-4 has sparked discussions on the advent of Artificial General Intelligence (AGI).
Ranked #5 on
Long-Context Understanding
on Ada-LEval (BestAnswer)
InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model
We introduce InternLM-XComposer2, a cutting-edge vision-language model excelling in free-form text-image composition and comprehension.
Ranked #18 on
Visual Question Answering
on MM-Vet v2
Beyond Hallucinations: Enhancing LVLMs through Hallucination-Aware Direct Preference Optimization
Multimodal large language models have made significant advancements in recent years, yet they still suffer from a common issue known as the "hallucination problem", in which the models generate textual descriptions that inaccurately depict or entirely fabricate content from associated images.
InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and Composition
We propose InternLM-XComposer, a vision-language large model that enables advanced image-text comprehension and composition.
Ranked #9 on
Visual Question Answering (VQA)
on InfiMM-Eval
MLLM-DataEngine: An Iterative Refinement Approach for MLLM
Despite the great advance of Multimodal Large Language Models (MLLMs) in both instruction dataset building and benchmarking, the independence of training and evaluation makes current MLLMs hard to further improve their capability under the guidance of evaluation results with a relatively low human cost.
