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Found 13 papers, 12 papers with code
Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization
To address this, we introduce a preference optimization (PO) process to enhance the multimodal reasoning capabilities of MLLMs.
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.
ChemVLM: Exploring the Power of Multimodal Large Language Models in Chemistry Area
We benchmark ChemVLM against a range of open-source and proprietary multimodal large language models on various tasks.
MMInstruct: A High-Quality Multi-Modal Instruction Tuning Dataset with Extensive Diversity
To construct MMInstruct, we propose an instruction generation data engine that leverages GPT-4V, GPT-3. 5, and manual correction.
Ranked #95 on
Visual Question Answering
on MM-Vet
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.
Needle In A Multimodal Haystack
In this work, we present Needle In A Multimodal Haystack (MM-NIAH), the first benchmark specifically designed to systematically evaluate the capability of existing MLLMs to comprehend long multimodal documents.
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 #11 on
Visual Question Answering
on MM-Vet v2
Vision-RWKV: Efficient and Scalable Visual Perception with RWKV-Like Architectures
Our evaluations demonstrate that VRWKV surpasses ViT's performance in image classification and has significantly faster speeds and lower memory usage processing high-resolution inputs.
The All-Seeing Project V2: Towards General Relation Comprehension of the Open World
In addition, we design a new benchmark, termed Circular-based Relation Probing Evaluation (CRPE) for comprehensively evaluating the relation comprehension capabilities of MLLMs.
Ranked #78 on
Visual Question Answering
on MM-Vet
MM-Interleaved: Interleaved Image-Text Generative Modeling via Multi-modal Feature Synchronizer
Developing generative models for interleaved image-text data has both research and practical value.
The All-Seeing Project: Towards Panoptic Visual Recognition and Understanding of the Open World
We present the All-Seeing (AS) project: a large-scale data and model for recognizing and understanding everything in the open world.
InternGPT: Solving Vision-Centric Tasks by Interacting with ChatGPT Beyond Language
Different from existing interactive systems that rely on pure language, by incorporating pointing instructions, the proposed iGPT significantly improves the efficiency of communication between users and chatbots, as well as the accuracy of chatbots in vision-centric tasks, especially in complicated visual scenarios where the number of objects is greater than 2.
Demystify Transformers & Convolutions in Modern Image Deep Networks
Our experiments on various tasks and an analysis of inductive bias show a significant performance boost due to advanced network-level and block-level designs, but performance differences persist among different STMs.
