263 citations · 724 across the 22 of their papers we have counts for
23 papers
A Generative Foundation Model for Chest Radiography
Yuanfeng Ji, Dan Lin, Xiyue Wang +12
The scarcity of well-annotated diverse medical images is a major hurdle for developing reliable AI models in healthcare. Substantial technical advances have been made in generative…
PixelFlow: Pixel-Space Generative Models with Flow
Shoufa Chen, Chongjian Ge, Shilong Zhang +2
We present PixelFlow, a family of image generation models that operate directly in the raw pixel space, in contrast to the predominant latent-space models. This approach simplifies…
Goku: Flow Based Video Generative Foundation Models
Shoufa Chen, Chongjian Ge, Yuqi Zhang +19
This paper introduces Goku, a state-of-the-art family of joint image-and-video generation models leveraging rectified flow Transformers to achieve industry-leading performance. We…
FlashVideo: Flowing Fidelity to Detail for Efficient High-Resolution Video Generation
Shilong Zhang, Wenbo Li, Shoufa Chen +7
DiT models have achieved great success in text-to-video generation, leveraging their scalability in model capacity and data scale. High content and motion fidelity aligned with tex…
Prompt-A-Video: Prompt Your Video Diffusion Model via Preference-Aligned LLM
Yatai Ji, Jiacheng Zhang, Jie Wu +9
Text-to-video models have made remarkable advancements through optimization on high-quality text-video pairs, where the textual prompts play a pivotal role in determining quality o…
CompGS: Unleashing 2D Compositionality for Compositional Text-to-3D via Dynamically Optimizing 3D Gaussians
Chongjian Ge, Chenfeng Xu, Yuanfeng Ji +6
Recent breakthroughs in text-guided image generation have significantly advanced the field of 3D generation. While generating a single high-quality 3D object is now feasible, gener…
PixArt-Σ: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation
Junsong Chen, Chongjian Ge, Enze Xie +7
In this paper, we introduce PixArt-Σ, a Diffusion Transformer model~(DiT) capable of directly generating images at 4K resolution. PixArt-Σrepresents a significant advancement over…
RoboCodeX: Multimodal Code Generation for Robotic Behavior Synthesis
Yao Mu, Junting Chen, Qinglong Zhang +16
Robotic behavior synthesis, the problem of understanding multimodal inputs and generating precise physical control for robots, is an important part of Embodied AI. Despite successe…
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…
Large Language Models as Automated Aligners for benchmarking Vision-Language Models
Yuanfeng Ji, Chongjian Ge, Weikai Kong +4
With the advancements in Large Language Models (LLMs), Vision-Language Models (VLMs) have reached a new level of sophistication, showing notable competence in executing intricate c…
InstructDET: Diversifying Referring Object Detection with Generalized Instructions
Ronghao Dang, Jiangyan Feng, Haodong Zhang +8
We propose InstructDET, a data-centric method for referring object detection (ROD) that localizes target objects based on user instructions. While deriving from referring expressio…
PixArt-: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis
Junsong Chen, Jincheng Yu, Chongjian Ge +8
The most advanced text-to-image (T2I) models require significant training costs (e.g., millions of GPU hours), seriously hindering the fundamental innovation for the AIGC community…
Speed Co-Augmentation for Unsupervised Audio-Visual Pre-training
Jiangliu Wang, Jianbo Jiao, Yibing Song +5
This work aims to improve unsupervised audio-visual pre-training. Inspired by the efficacy of data augmentation in visual contrastive learning, we propose a novel speed co-augmenta…
MetaBEV: Solving Sensor Failures for BEV Detection and Map Segmentation
Chongjian Ge, Junsong Chen, Enze Xie +5
Perception systems in modern autonomous driving vehicles typically take inputs from complementary multi-modal sensors, e.g., LiDAR and cameras. However, in real-world applications,…
DeepAccident: A Motion and Accident Prediction Benchmark for V2X Autonomous Driving
Tianqi Wang, Sukmin Kim, Wenxuan Ji +5
Safety is the primary priority of autonomous driving. Nevertheless, no published dataset currently supports the direct and explainable safety evaluation for autonomous driving. In…
Soft Neighbors are Positive Supporters in Contrastive Visual Representation Learning
Chongjian Ge, Jiangliu Wang, Zhan Tong +3
Contrastive learning methods train visual encoders by comparing views from one instance to others. Typically, the views created from one instance are set as positive, while views f…
AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation
Yuanfeng Ji, Haotian Bai, Jie Yang +8
Despite the considerable progress in automatic abdominal multi-organ segmentation from CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is hampe…
AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition
Shoufa Chen, Chongjian Ge, Zhan Tong +4
Pretraining Vision Transformers (ViTs) has achieved great success in visual recognition. A following scenario is to adapt a ViT to various image and video recognition tasks. The ad…
Not All Patches are What You Need: Expediting Vision Transformers via Token Reorganizations
Youwei Liang, Chongjian Ge, Zhan Tong +3
Vision Transformers (ViTs) take all the image patches as tokens and construct multi-head self-attention (MHSA) among them. Complete leverage of these image tokens brings redundant…
Revitalizing CNN Attentions via Transformers in Self-Supervised Visual Representation Learning
Chongjian Ge, Youwei Liang, Yibing Song +3
Studies on self-supervised visual representation learning (SSL) improve encoder backbones to discriminate training samples without labels. While CNN encoders via SSL achieve compar…
CycleMLP: A MLP-like Architecture for Dense Prediction
Shoufa Chen, Enze Xie, Chongjian Ge +3
This paper presents a simple MLP-like architecture, CycleMLP, which is a versatile backbone for visual recognition and dense predictions. As compared to modern MLP architectures, e…
Disentangled Cycle Consistency for Highly-realistic Virtual Try-On
Chongjian Ge, Yibing Song, Yuying Ge +3
Image virtual try-on replaces the clothes on a person image with a desired in-shop clothes image. It is challenging because the person and the in-shop clothes are unpaired. Existin…
Parser-Free Virtual Try-on via Distilling Appearance Flows
Yuying Ge, Yibing Song, Ruimao Zhang +3
Image virtual try-on aims to fit a garment image (target clothes) to a person image. Prior methods are heavily based on human parsing. However, slightly-wrong segmentation results…