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Found 8 papers, 6 papers with code
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.
ArchCAD-400K: An Open Large-Scale Architectural CAD Dataset and New Baseline for Panoptic Symbol Spotting
Recognizing symbols in architectural CAD drawings is critical for various advanced engineering applications.
VisualPRM: An Effective Process Reward Model for Multimodal Reasoning
We introduce VisualPRM, an advanced multimodal Process Reward Model (PRM) with 8B parameters, which improves the reasoning abilities of existing Multimodal Large Language Models (MLLMs) across different model scales and families with Best-of-N (BoN) evaluation strategies.
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
SDAN: Squared Deformable Alignment Network for Learning Misaligned Optical Zoom
Deep Neural Network (DNN) based super-resolution algorithms have greatly improved the quality of the generated images.
