12 citations · 31 across the 11 of their papers we have counts for
11 papers
Monkey King Bang: A Unified Scientific Multimodal Foundation Model
Hesen Chen, Xinyu Su, Xiaomeng Yang +11
Scientific discovery is increasingly shifting from isolated disciplines to multi-domain reasoning, and AI for science faces a similar transition. Existing systems are either specia…
Omni-Video 2: Scaling MLLM-Conditioned Diffusion for Unified Video Generation and Editing
Hao Yang, Zhiyu Tan, Jia Gong +7
We present Omni-Video 2, a scalable and computationally efficient model that connects pretrained multimodal large-language models (MLLMs) with video diffusion models for unified vi…
DiverseDiT: Towards Diverse Representation Learning in Diffusion Transformers
Mengping Yang, Zhiyu Tan, Binglei Li +3
Recent breakthroughs in Diffusion Transformers (DiTs) have revolutionized the field of visual synthesis due to their superior scalability. To facilitate DiTs' capability of capturi…
SARA: Structural and Adversarial Representation Alignment for Training-efficient Diffusion Models
Hesen Chen, Junyan Wang, Zhiyu Tan +1
Modern diffusion models encounter a fundamental trade-off between training efficiency and generation quality. While existing representation alignment methods, such as REPA, acceler…
Raccoon: Multi-stage Diffusion Training with Coarse-to-Fine Curating Videos
Zhiyu Tan, Junyan Wang, Hao Yang +4
Text-to-video generation has demonstrated promising progress with the advent of diffusion models, yet existing approaches are limited by dataset quality and computational resources…
E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models
Zhiyu Tan, WenXu Qian, Hesen Chen +3
Diffusion models have established themselves as the de facto primary paradigm in visual generative modeling, revolutionizing the field through remarkable success across various div…
Data-Juicer: A One-Stop Data Processing System for Large Language Models
Daoyuan Chen, Yilun Huang, Zhijian Ma +10
The immense evolution in Large Language Models (LLMs) has underscored the importance of massive, heterogeneous, and high-quality data. A data recipe is a mixture of data from diffe…
PreNAS: Preferred One-Shot Learning Towards Efficient Neural Architecture Search
Haibin Wang, Ce Ge, Hesen Chen +1
The wide application of pre-trained models is driving the trend of once-for-all training in one-shot neural architecture search (NAS). However, training within a huge sample space…
Fine-Grained AutoAugmentation for Multi-Label Classification
Ya Wang, Hesen Chen, Fangyi Zhang +4
Data augmentation is a commonly used approach to improving the generalization of deep learning models. Recent works show that learned data augmentation policies can achieve better…
Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition
Ming Lin, Pichao Wang, Zhenhong Sun +5
Accuracy predictor is a key component in Neural Architecture Search (NAS) for ranking architectures. Building a high-quality accuracy predictor usually costs enormous computation.…
Neural Architecture Design for GPU-Efficient Networks
Ming Lin, Hesen Chen, Xiuyu Sun +3
Many mission-critical systems are based on GPU for inference. It requires not only high recognition accuracy but also low latency in responding time. Although many studies are devo…