35 citations · 71 across the 12 of their papers we have counts for
12 papers
Seed1.5-VL Technical Report
Dong Guo, Faming Wu, Feida Zhu +194
We present Seed1.5-VL, a vision-language foundation model designed to advance general-purpose multimodal understanding and reasoning. Seed1.5-VL is composed with a 532M-parameter v…
Vision Search Assistant: Empower Vision-Language Models as Multimodal Search Engines
Zhixin Zhang, Yiyuan Zhang, Xiaohan Ding +1
Search engines enable the retrieval of unknown information with texts. However, traditional methods fall short when it comes to understanding unfamiliar visual content, such as ide…
Scaling Up Your Kernels: Large Kernel Design in ConvNets towards Universal Representations
Yiyuan Zhang, Xiaohan Ding, Xiangyu Yue
This paper proposes the paradigm of large convolutional kernels in designing modern Convolutional Neural Networks (ConvNets). We establish that employing a few large kernels, inste…
SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation
Yuying Ge, Sijie Zhao, Jinguo Zhu +6
The rapid evolution of multimodal foundation model has demonstrated significant progresses in vision-language understanding and generation, e.g., our previous work SEED-LLaMA. Howe…
InteractiveVideo: User-Centric Controllable Video Generation with Synergistic Multimodal Instructions
Yiyuan Zhang, Yuhao Kang, Zhixin Zhang +3
We introduce , a user-centric framework for video generation. Different from traditional generative approaches that operate based on user-provided images…
Multimodal Pathway: Improve Transformers with Irrelevant Data from Other Modalities
Yiyuan Zhang, Xiaohan Ding, Kaixiong Gong +3
We propose to improve transformers of a specific modality with irrelevant data from other modalities, e.g., improve an ImageNet model with audio or point cloud datasets. We would l…
VL-GPT: A Generative Pre-trained Transformer for Vision and Language Understanding and Generation
Jinguo Zhu, Xiaohan Ding, Yixiao Ge +5
In this work, we introduce Vision-Language Generative Pre-trained Transformer (VL-GPT), a transformer model proficient at concurrently perceiving and generating visual and linguist…
Online Vectorized HD Map Construction using Geometry
Zhixin Zhang, Yiyuan Zhang, Xiaohan Ding +2
The construction of online vectorized High-Definition (HD) maps is critical for downstream prediction and planning. Recent efforts have built strong baselines for this task, howeve…
Advancing Vision Transformers with Group-Mix Attention
Chongjian Ge, Xiaohan Ding, Zhan Tong +4
Vision Transformers (ViTs) have been shown to enhance visual recognition through modeling long-range dependencies with multi-head self-attention (MHSA), which is typically formulat…
UniRepLKNet: A Universal Perception Large-Kernel ConvNet for Audio, Video, Point Cloud, Time-Series and Image Recognition
Xiaohan Ding, Yiyuan Zhang, Yixiao Ge +4
Large-kernel convolutional neural networks (ConvNets) have recently received extensive research attention, but two unresolved and critical issues demand further investigation. 1) T…
RefConv: Re-parameterized Refocusing Convolution for Powerful ConvNets
Zhicheng Cai, Xiaohan Ding, Qiu Shen +1
We propose Re-parameterized Refocusing Convolution (RefConv) as a replacement for regular convolutional layers, which is a plug-and-play module to improve the performance without a…
Towards Unified and Effective Domain Generalization
Yiyuan Zhang, Kaixiong Gong, Xiaohan Ding +4
We propose , a novel and fied framework for omain eneralization that is capable of significantly enhancing the out-of-distribu…