19 citations · 40 across the 18 of their papers we have counts for
19 papers
TIE: Time Interval Encoding for Video Generation over Events
Zhilei Shu, Shangwen Zhu, Zihang Liang +10
Director-style prompting, robotic action prediction, and interactive video agents demand temporal grounding over concurrent events -- a regime in which 68% of general clips and ove…
BenchHAR: Benchmarking Self-Supervised Learning for Generalizable Sensor-based Activity Recognition
Yize Cai, Rui Feng, Anlan Yu +2
Human Activity Recognition (HAR) from wearable sensors supports broad healthcare and behavior science applications. However, data heterogeneity and the scarcity of labeled data lim…
Instability in Diffusion ODEs: An Explanation for Inaccurate Image Reconstruction
Han Zhang, Jinghong Mao, Shangwen Zhu +6
Diffusion reconstruction plays a critical role in various applications such as image editing, restoration, and style transfer. In theory, the reconstruction should be simple - it j…
RAIN: Real-time Animation of Infinite Video Stream
Zhilei Shu, Ruili Feng, Yang Cao +1
Live animation has gained immense popularity for enhancing online engagement, yet achieving high-quality, real-time, and stable animation with diffusion models remains challenging,…
The Matrix: Infinite-Horizon World Generation with Real-Time Moving Control
Ruili Feng, Han Zhang, Zhantao Yang +7
We present The Matrix, the first foundational realistic world simulator capable of generating continuous 720p high-fidelity real-scene video streams with real-time, responsive cont…
BACON: Improving Clarity of Image Captions via Bag-of-Concept Graphs
Zhantao Yang, Ruili Feng, Keyu Yan +13
Advancements in large Vision-Language Models have brought precise, accurate image captioning, vital for advancing multi-modal image understanding and processing. Yet these captions…
Regularized Mask Tuning: Uncovering Hidden Knowledge in Pre-trained Vision-Language Models
Kecheng Zheng, Wei Wu, Ruili Feng +6
Prompt tuning and adapter tuning have shown great potential in transferring pre-trained vision-language models (VLMs) to various downstream tasks. In this work, we design a new typ…
Lipschitz Singularities in Diffusion Models
Zhantao Yang, Ruili Feng, Han Zhang +8
Diffusion models, which employ stochastic differential equations to sample images through integrals, have emerged as a dominant class of generative models. However, the rationality…
Cones 2: Customizable Image Synthesis with Multiple Subjects
Zhiheng Liu, Yifei Zhang, Yujun Shen +7
Synthesizing images with user-specified subjects has received growing attention due to its practical applications. Despite the recent success in single subject customization, exist…
Cones: Concept Neurons in Diffusion Models for Customized Generation
Zhiheng Liu, Ruili Feng, Kai Zhu +6
Human brains respond to semantic features of presented stimuli with different neurons. It is then curious whether modern deep neural networks admit a similar behavior pattern. Spec…
DIFFER: Decomposing Individual Reward for Fair Experience Replay in Multi-Agent Reinforcement Learning
Xunhan Hu, Jian Zhao, Wengang Zhou +2
Cooperative multi-agent reinforcement learning (MARL) is a challenging task, as agents must learn complex and diverse individual strategies from a shared team reward. However, exis…
Dimensionality-Varying Diffusion Process
Han Zhang, Ruili Feng, Zhantao Yang +7
Diffusion models, which learn to reverse a signal destruction process to generate new data, typically require the signal at each step to have the same dimension. We argue that, con…
Neural Dependencies Emerging from Learning Massive Categories
Ruili Feng, Kecheng Zheng, Kai Zhu +7
This work presents two astonishing findings on neural networks learned for large-scale image classification. 1) Given a well-trained model, the logits predicted for some category c…
Rank Diminishing in Deep Neural Networks
Ruili Feng, Kecheng Zheng, Yukun Huang +3
The rank of neural networks measures information flowing across layers. It is an instance of a key structural condition that applies across broad domains of machine learning. In pa…
Context Attention Network for Skeleton Extraction
Zixuan Huang, Yunfeng Wang, Zhiwen Chen +3
Skeleton extraction is a task focused on providing a simple representation of an object by extracting the skeleton from the given binary or RGB image. In recent years many attracti…
Principled Knowledge Extrapolation with GANs
Ruili Feng, Jie Xiao, Kecheng Zheng +4
Human can extrapolate well, generalize daily knowledge into unseen scenarios, raise and answer counterfactual questions. To imitate this ability via generative models, previous wor…
Weakly Supervised High-Fidelity Clothing Model Generation
Ruili Feng, Cheng Ma, Chengji Shen +5
The development of online economics arouses the demand of generating images of models on product clothes, to display new clothes and promote sales. However, the expensive proprieta…
Low-Rank Subspaces in GANs
Jiapeng Zhu, Ruili Feng, Yujun Shen +4
The latent space of a Generative Adversarial Network (GAN) has been shown to encode rich semantics within some subspaces. To identify these subspaces, researchers typically analyze…
On Noise Injection in Generative Adversarial Networks
Ruili Feng, Deli Zhao, Zhengjun Zha
Noise injection has been proved to be one of the key technique advances in generating high-fidelity images. Despite its successful usage in GANs, the mechanism of its validity is s…