21 citations · 83 across the 19 of their papers we have counts for
15 papers · 1 filter
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…
StreamChat: Chatting with Streaming Video
Jihao Liu, Zhiding Yu, Shiyi Lan +5
This paper presents StreamChat, a novel approach that enhances the interaction capabilities of Large Multimodal Models (LMMs) with streaming video content. In streaming interaction…
MM-Instruct: Generated Visual Instructions for Large Multimodal Model Alignment
Jihao Liu, Xin Huang, Jinliang Zheng +5
This paper introduces MM-Instruct, a large-scale dataset of diverse and high-quality visual instruction data designed to enhance the instruction-following capabilities of large mul…
Enhancing Vision-Language Model with Unmasked Token Alignment
Jihao Liu, Jinliang Zheng, Boxiao Liu +2
Contrastive pre-training on image-text pairs, exemplified by CLIP, becomes a standard technique for learning multi-modal visual-language representations. Although CLIP has demonstr…
Instruction-Guided Visual Masking
Jinliang Zheng, Jianxiong Li, Sijie Cheng +6
Instruction following is crucial in contemporary LLM. However, when extended to multimodal setting, it often suffers from misalignment between specific textual instruction and targ…
GLID: Pre-training a Generalist Encoder-Decoder Vision Model
Jihao Liu, Jinliang Zheng, Yu Liu +1
This paper proposes a GeneraLIst encoder-Decoder (GLID) pre-training method for better handling various downstream computer vision tasks. While self-supervised pre-training approac…
GeoMIM: Towards Better 3D Knowledge Transfer via Masked Image Modeling for Multi-view 3D Understanding
Jihao Liu, Tai Wang, Boxiao Liu +3
Multi-view camera-based 3D detection is a challenging problem in computer vision. Recent works leverage a pretrained LiDAR detection model to transfer knowledge to a camera-based s…
TokenMix: Rethinking Image Mixing for Data Augmentation in Vision Transformers
Jihao Liu, Boxiao Liu, Hang Zhou +2
CutMix is a popular augmentation technique commonly used for training modern convolutional and transformer vision networks. It was originally designed to encourage Convolution Neur…
UniNet: Unified Architecture Search with Convolution, Transformer, and MLP
Jihao Liu, Xin Huang, Guanglu Song +2
Recently, transformer and multi-layer perceptron (MLP) architectures have achieved impressive results on various vision tasks. However, how to effectively combine those operators t…
MixMAE: Mixed and Masked Autoencoder for Efficient Pretraining of Hierarchical Vision Transformers
Jihao Liu, Xin Huang, Jinliang Zheng +2
In this paper, we propose Mixed and Masked AutoEncoder (MixMAE), a simple but efficient pretraining method that is applicable to various hierarchical Vision Transformers. Existing…
INTERN: A New Learning Paradigm Towards General Vision
Jing Shao, Siyu Chen, Yangguang Li +24
Enormous waves of technological innovations over the past several years, marked by the advances in AI technologies, are profoundly reshaping the industry and the society. However,…
UniNet: Unified Architecture Search with Convolution, Transformer, and MLP
Jihao Liu, Hongsheng Li, Guanglu Song +2
Recently, transformer and multi-layer perceptron (MLP) architectures have achieved impressive results on various vision tasks. A few works investigated manually combining those ope…
Rotate-and-Render: Unsupervised Photorealistic Face Rotation from Single-View Images
Hang Zhou, Jihao Liu, Ziwei Liu +2
Though face rotation has achieved rapid progress in recent years, the lack of high-quality paired training data remains a great hurdle for existing methods. The current generative…
Learning Where to Focus for Efficient Video Object Detection
Zhengkai Jiang, Yu Liu, Ceyuan Yang +5
Transferring existing image-based detectors to the video is non-trivial since the quality of frames is always deteriorated by part occlusion, rare pose, and motion blur. Previous a…
Towards Flops-constrained Face Recognition
Yu Liu, Guanglu Song, Manyuan Zhang +3
Large scale face recognition is challenging especially when the computational budget is limited. Given a \textit{flops} upper bound, the key is to find the optimal neural network a…