205 citations · 527 across the 22 of their papers we have counts for
22 papers
How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites
Zhe Chen, Weiyun Wang, Hao Tian +32
In this report, we introduce InternVL 1.5, an open-source multimodal large language model (MLLM) to bridge the capability gap between open-source and proprietary commercial models…
InternLM-XComposer2-4KHD: A Pioneering Large Vision-Language Model Handling Resolutions from 336 Pixels to 4K HD
Xiaoyi Dong, Pan Zhang, Yuhang Zang +21
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…
Bounding Box Stability against Feature Dropout Reflects Detector Generalization across Environments
Yang Yang, Wenhai Wang, Zhe Chen +2
Bounding boxes uniquely characterize object detection, where a good detector gives accurate bounding boxes of categories of interest. However, in the real-world where test ground t…
Video Mamba Suite: State Space Model as a Versatile Alternative for Video Understanding
Guo Chen, Yifei Huang, Jilan Xu +7
Understanding videos is one of the fundamental directions in computer vision research, with extensive efforts dedicated to exploring various architectures such as RNN, 3D CNN, and…
Vision-RWKV: Efficient and Scalable Visual Perception with RWKV-Like Architectures
Yuchen Duan, Weiyun Wang, Zhe Chen +7
Transformers have revolutionized computer vision and natural language processing, but their high computational complexity limits their application in high-resolution image processi…
The All-Seeing Project V2: Towards General Relation Comprehension of the Open World
Weiyun Wang, Yiming Ren, Haowen Luo +9
We present the All-Seeing Project V2: a new model and dataset designed for understanding object relations in images. Specifically, we propose the All-Seeing Model V2 (ASMv2) that i…
InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks
Zhe Chen, Jiannan Wu, Wenhai Wang +12
The exponential growth of large language models (LLMs) has opened up numerous possibilities for multimodal AGI systems. However, the progress in vision and vision-language foundati…
MM-Interleaved: Interleaved Image-Text Generative Modeling via Multi-modal Feature Synchronizer
Changyao Tian, Xizhou Zhu, Yuwen Xiong +10
Developing generative models for interleaved image-text data has both research and practical value. It requires models to understand the interleaved sequences and subsequently gene…
The All-Seeing Project: Towards Panoptic Visual Recognition and Understanding of the Open World
Weiyun Wang, Min Shi, Qingyun Li +11
We present the All-Seeing (AS) project: a large-scale data and model for recognizing and understanding everything in the open world. Using a scalable data engine that incorporates…
AVSegFormer: Audio-Visual Segmentation with Transformer
Shengyi Gao, Zhe Chen, Guo Chen +2
The combination of audio and vision has long been a topic of interest in the multi-modal community. Recently, a new audio-visual segmentation (AVS) task has been introduced, aiming…
InternGPT: Solving Vision-Centric Tasks by Interacting with ChatGPT Beyond Language
Zhaoyang Liu, Yinan He, Wenhai Wang +17
We present an interactive visual framework named InternGPT, or iGPT for short. The framework integrates chatbots that have planning and reasoning capabilities, such as ChatGPT, wit…
GeoDiffusion: Text-Prompted Geometric Control for Object Detection Data Generation
Kai Chen, Enze Xie, Zhe Chen +4
Diffusion models have attracted significant attention due to the remarkable ability to create content and generate data for tasks like image classification. However, the usage of d…
VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Tasks
Wenhai Wang, Zhe Chen, Xiaokang Chen +8
Large language models (LLMs) have notably accelerated progress towards artificial general intelligence (AGI), with their impressive zero-shot capacity for user-tailored tasks, endo…
Graph Propagation Transformer for Graph Representation Learning
Zhe Chen, Hao Tan, Tao Wang +5
This paper presents a novel transformer architecture for graph representation learning. The core insight of our method is to fully consider the information propagation among nodes…
DDP: Diffusion Model for Dense Visual Prediction
Yuanfeng Ji, Zhe Chen, Enze Xie +6
We propose a simple, efficient, yet powerful framework for dense visual predictions based on the conditional diffusion pipeline. Our approach follows a "noise-to-map" generative pa…
Champion Solution for the WSDM2023 Toloka VQA Challenge
Shengyi Gao, Zhe Chen, Guo Chen +2
In this report, we present our champion solution to the WSDM2023 Toloka Visual Question Answering (VQA) Challenge. Different from the common VQA and visual grounding (VG) tasks, th…
InternVideo-Ego4D: A Pack of Champion Solutions to Ego4D Challenges
Guo Chen, Sen Xing, Zhe Chen +18
In this report, we present our champion solutions to five tracks at Ego4D challenge. We leverage our developed InternVideo, a video foundation model, for five Ego4D tasks, includin…
InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions
Wenhai Wang, Jifeng Dai, Zhe Chen +9
Compared to the great progress of large-scale vision transformers (ViTs) in recent years, large-scale models based on convolutional neural networks (CNNs) are still in an early sta…
Vision Transformer Adapter for Dense Predictions
Zhe Chen, Yuchen Duan, Wenhai Wang +4
This work investigates a simple yet powerful dense prediction task adapter for Vision Transformer (ViT). Unlike recently advanced variants that incorporate vision-specific inductiv…
FAST: Faster Arbitrarily-Shaped Text Detector with Minimalist Kernel Representation
Zhe Chen, Jiahao Wang, Wenhai Wang +4
We propose an accurate and efficient scene text detection framework, termed FAST (i.e., faster arbitrarily-shaped text detector). Different from recent advanced text detectors that…
Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization
Zhe Chen, Wenhai Wang, Enze Xie +2
We present an extremely simple Ultra-Resolution Style Transfer framework, termed URST, to flexibly process arbitrary high-resolution images (e.g., 10000x10000 pixels) style transfe…
Block Shuffle: A Method for High-resolution Fast Style Transfer with Limited Memory
Weifeng Ma, Zhe Chen, Caoting Ji
Fast Style Transfer is a series of Neural Style Transfer algorithms that use feed-forward neural networks to render input images. Because of the high dimension of the output layer,…