50 citations · 358 across the 37 of their papers we have counts for
37 papers
DreamVAR: Taming Reinforced Visual Autoregressive Model for High-Fidelity Subject-Driven Image Generation
Xin Jiang, Jingwen Chen, Yehao Li +5
Recent advances in subject-driven image generation using diffusion models have attracted considerable attention for their remarkable capabilities in producing high-quality images.…
Visual Autoregressive Modeling for Instruction-Guided Image Editing
Qingyang Mao, Qi Cai, Yehao Li +5
Recent advances in diffusion models have brought remarkable visual fidelity to instruction-guided image editing. However, their global denoising process inherently entangles the ed…
HiDream-I1: A High-Efficient Image Generative Foundation Model with Sparse Diffusion Transformer
Qi Cai, Jingwen Chen, Yang Chen +19
Recent advancements in image generative foundation models have prioritized quality improvements but often at the cost of increased computational complexity and inference latency. T…
Hierarchical Masked Autoregressive Models with Low-Resolution Token Pivots
Guangting Zheng, Yehao Li, Yingwei Pan +4
Autoregressive models have emerged as a powerful generative paradigm for visual generation. The current de-facto standard of next token prediction commonly operates over a single-s…
Unleashing Text-to-Image Diffusion Prior for Zero-Shot Image Captioning
Jianjie Luo, Jingwen Chen, Yehao Li +4
Recently, zero-shot image captioning has gained increasing attention, where only text data is available for training. The remarkable progress in text-to-image diffusion model prese…
Improving Text-guided Object Inpainting with Semantic Pre-inpainting
Yifu Chen, Jingwen Chen, Yingwei Pan +4
Recent years have witnessed the success of large text-to-image diffusion models and their remarkable potential to generate high-quality images. The further pursuit of enhancing the…
Improving Virtual Try-On with Garment-focused Diffusion Models
Siqi Wan, Yehao Li, Jingwen Chen +4
Diffusion models have led to the revolutionizing of generative modeling in numerous image synthesis tasks. Nevertheless, it is not trivial to directly apply diffusion models for sy…
Boosting Diffusion Models with Moving Average Sampling in Frequency Domain
Yurui Qian, Qi Cai, Yingwei Pan +4
Diffusion models have recently brought a powerful revolution in image generation. Despite showing impressive generative capabilities, most of these models rely on the current sampl…
SD-DiT: Unleashing the Power of Self-supervised Discrimination in Diffusion Transformer
Rui Zhu, Yingwei Pan, Yehao Li +4
Diffusion Transformer (DiT) has emerged as the new trend of generative diffusion models on image generation. In view of extremely slow convergence in typical DiT, recent breakthrou…
HIRI-ViT: Scaling Vision Transformer with High Resolution Inputs
Ting Yao, Yehao Li, Yingwei Pan +1
The hybrid deep models of Vision Transformer (ViT) and Convolution Neural Network (CNN) have emerged as a powerful class of backbones for vision tasks. Scaling up the input resolut…
Control3D: Towards Controllable Text-to-3D Generation
Yang Chen, Yingwei Pan, Yehao Li +2
Recent remarkable advances in large-scale text-to-image diffusion models have inspired a significant breakthrough in text-to-3D generation, pursuing 3D content creation solely from…
Semantic-Conditional Diffusion Networks for Image Captioning
Jianjie Luo, Yehao Li, Yingwei Pan +4
Recent advances on text-to-image generation have witnessed the rise of diffusion models which act as powerful generative models. Nevertheless, it is not trivial to exploit such lat…
SPE-Net: Boosting Point Cloud Analysis via Rotation Robustness Enhancement
Zhaofan Qiu, Yehao Li, Yu Wang +3
In this paper, we propose a novel deep architecture tailored for 3D point cloud applications, named as SPE-Net. The embedded ``Selective Position Encoding (SPE)'' procedure relies…
Dual Vision Transformer
Ting Yao, Yehao Li, Yingwei Pan +3
Prior works have proposed several strategies to reduce the computational cost of self-attention mechanism. Many of these works consider decomposing the self-attention procedure int…
Wave-ViT: Unifying Wavelet and Transformers for Visual Representation Learning
Ting Yao, Yingwei Pan, Yehao Li +2
Multi-scale Vision Transformer (ViT) has emerged as a powerful backbone for computer vision tasks, while the self-attention computation in Transformer scales quadratically w.r.t. t…
Comprehending and Ordering Semantics for Image Captioning
Yehao Li, Yingwei Pan, Ting Yao +1
Comprehending the rich semantics in an image and ordering them in linguistic order are essential to compose a visually-grounded and linguistically coherent description for image ca…
Silver-Bullet-3D at ManiSkill 2021: Learning-from-Demonstrations and Heuristic Rule-based Methods for Object Manipulation
Yingwei Pan, Yehao Li, Yiheng Zhang +5
This paper presents an overview and comparative analysis of our systems designed for the following two tracks in SAPIEN ManiSkill Challenge 2021: No Interaction Track: The No Inter…
Uni-EDEN: Universal Encoder-Decoder Network by Multi-Granular Vision-Language Pre-training
Yehao Li, Jiahao Fan, Yingwei Pan +3
Vision-language pre-training has been an emerging and fast-developing research topic, which transfers multi-modal knowledge from rich-resource pre-training task to limited-resource…
CoCo-BERT: Improving Video-Language Pre-training with Contrastive Cross-modal Matching and Denoising
Jianjie Luo, Yehao Li, Yingwei Pan +3
BERT-type structure has led to the revolution of vision-language pre-training and the achievement of state-of-the-art results on numerous vision-language downstream tasks. Existing…
X-modaler: A Versatile and High-performance Codebase for Cross-modal Analytics
Yehao Li, Yingwei Pan, Jingwen Chen +2
With the rise and development of deep learning over the past decade, there has been a steady momentum of innovation and breakthroughs that convincingly push the state-of-the-art of…
Contextual Transformer Networks for Visual Recognition
Yehao Li, Ting Yao, Yingwei Pan +1
Transformer with self-attention has led to the revolutionizing of natural language processing field, and recently inspires the emergence of Transformer-style architecture design wi…
Scheduled Sampling in Vision-Language Pretraining with Decoupled Encoder-Decoder Network
Yehao Li, Yingwei Pan, Ting Yao +2
Despite having impressive vision-language (VL) pretraining with BERT-based encoder for VL understanding, the pretraining of a universal encoder-decoder for both VL understanding an…
Pre-training for Video Captioning Challenge 2020 Summary
Yingwei Pan, Jun Xu, Yehao Li +2
The Pre-training for Video Captioning Challenge 2020 Summary: results and challenge participants' technical reports.
Auto-captions on GIF: A Large-scale Video-sentence Dataset for Vision-language Pre-training
Yingwei Pan, Yehao Li, Jianjie Luo +3
In this work, we present Auto-captions on GIF, which is a new large-scale pre-training dataset for generic video understanding. All video-sentence pairs are created by automaticall…