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Found 8 papers, 6 papers with code
Consensus Entropy: Harnessing Multi-VLM Agreement for Self-Verifying and Self-Improving OCR
We introduce Consensus Entropy (CE), a training-free post-inference method that quantifies OCR uncertainty by aggregating outputs from multiple VLMs.
Optical Character Recognition
Optical Character Recognition (OCR)
InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models
We introduce InternVL3, a significant advancement in the InternVL series featuring a native multimodal pre-training paradigm.
Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling
We introduce InternVL 2. 5, an advanced multimodal large language model (MLLM) series that builds upon InternVL 2. 0, maintaining its core model architecture while introducing significant enhancements in training and testing strategies as well as data quality.
Ranked #1 on
Video Question Answering
on NExT-QA
Mini-InternVL: A Flexible-Transfer Pocket Multimodal Model with 5% Parameters and 90% Performance
Multimodal large language models (MLLMs) have demonstrated impressive performance in vision-language tasks across a broad spectrum of domains.
OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text
In this paper, we introduce OmniCorpus, a 10 billion-scale image-text interleaved dataset.
How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites
Compared to both open-source and proprietary models, InternVL 1. 5 shows competitive performance, achieving state-of-the-art results in 8 of 18 benchmarks.
Ranked #6 on
Multiple-choice
on Neptune-Full
Teaching MLP More Graph Information: A Three-stage Multitask Knowledge Distillation Framework
To the best of our knowledge, it is the first work to include hidden layer distillation for student MLP on graphs and to combine graph Positional Encoding with MLP.
ControlLLM: Augment Language Models with Tools by Searching on Graphs
We present ControlLLM, a novel framework that enables large language models (LLMs) to utilize multi-modal tools for solving complex real-world tasks.
