70 citations · 142 across the 20 of their papers we have counts for
23 papers
Routing Policy Optimization for Online Diffusion Reinforcement Learning
Qiuhong Shen, Yiding Yang, Yuxin Zhang +8
Reinforcement learning can align visual generators with reward signals beyond their pretraining objectives, but existing score-function methods typically introduce stochasticity by…
ScribbleEdit: A Benchmark for Scribble-Only Image Editing
Jie Ren, Hao Kang, Kai Guo +9
Scribble-based interaction provides a lightweight and intuitive way for users to specify image editing intents in interactive editing tools. However, current image editing models b…
ActWorld: From Explorable to Interactive World Model via Action-Aware Memory
Zhexiao Xiong, Yizhi Song, Hao Kang +11
Interactive world models aim to simulate environment dynamics under real-time user actions. However, their action vocabulary is largely confined to navigation: most actions corresp…
FSVideo: Fast Speed Video Diffusion Model in a Highly-Compressed Latent Space
FSVideo Team, Qingyu Chen, Zhiyuan Fang +17
We introduce FSVideo, a fast speed transformer-based image-to-video (I2V) diffusion framework. We build our framework on the following key components: 1.) a new video autoencoder w…
VIVA: VLM-Guided Instruction-Based Video Editing with Reward Optimization
Xiaoyan Cong, Haotian Yang, Angtian Wang +4
Instruction-based video editing aims to modify an input video according to a natural-language instruction while preserving content fidelity and temporal coherence. However, existin…
TGT: Text-Grounded Trajectories for Locally Controlled Video Generation
Guofeng Zhang, Angtian Wang, Jacob Zhiyuan Fang +8
Text-to-video generation has advanced rapidly in visual fidelity, whereas standard methods still have limited ability to control the subject composition of generated scenes. Prior…
ATI: Any Trajectory Instruction for Controllable Video Generation
Angtian Wang, Haibin Huang, Jacob Zhiyuan Fang +2
We propose a unified framework for motion control in video generation that seamlessly integrates camera movement, object-level translation, and fine-grained local motion using traj…
MAGREF: Masked Guidance for Any-Reference Video Generation with Subject Disentanglement
Yufan Deng, Yuanyang Yin, Xun Guo +8
We tackle the task of any-reference video generation, which aims to synthesize videos conditioned on arbitrary types and combinations of reference subjects, together with textual p…
CINEMA: Coherent Multi-Subject Video Generation via MLLM-Based Guidance
Yufan Deng, Xun Guo, Yizhi Wang +7
Video generation has witnessed remarkable progress with the advent of deep generative models, particularly diffusion models. While existing methods excel in generating high-quality…
Deployment Prior Injection for Run-time Calibratable Object Detection
Mo Zhou, Yiding Yang, Haoxiang Li +2
With a strong alignment between the training and test distributions, object relation as a context prior facilitates object detection. Yet, it turns into a harmful but inevitable tr…
UGG: Unified Generative Grasping
Jiaxin Lu, Hao Kang, Haoxiang Li +4
Dexterous grasping aims to produce diverse grasping postures with a high grasping success rate. Regression-based methods that directly predict grasping parameters given the object…
Deep Graph Reprogramming
Yongcheng Jing, Chongbin Yuan, Li Ju +3
In this paper, we explore a novel model reusing task tailored for graph neural networks (GNNs), termed as "deep graph reprogramming". We strive to reprogram a pre-trained GNN, with…
Learning Graph Neural Networks for Image Style Transfer
Yongcheng Jing, Yining Mao, Yiding Yang +4
State-of-the-art parametric and non-parametric style transfer approaches are prone to either distorted local style patterns due to global statistics alignment, or unpleasing artifa…
Meta-Aggregator: Learning to Aggregate for 1-bit Graph Neural Networks
Yongcheng Jing, Yiding Yang, Xinchao Wang +2
In this paper, we study a novel meta aggregation scheme towards binarizing graph neural networks (GNNs). We begin by developing a vanilla 1-bit GNN framework that binarizes both th…
Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Tracking
Yiding Yang, Zhou Ren, Haoxiang Li +3
Multi-person pose estimation and tracking serve as crucial steps for video understanding. Most state-of-the-art approaches rely on first estimating poses in each frame and only the…
VOLDOR: Visual Odometry from Log-logistic Dense Optical flow Residuals
Zhixiang Min, Yiding Yang, Enrique Dunn
We propose a dense indirect visual odometry method taking as input externally estimated optical flow fields instead of hand-crafted feature correspondences. We define our problem a…
SPAGAN: Shortest Path Graph Attention Network
Yiding Yang, Xinchao Wang, Mingli Song +2
Graph convolutional networks (GCN) have recently demonstrated their potential in analyzing non-grid structure data that can be represented as graphs. The core idea is to encode the…
Overcoming Catastrophic Forgetting in Graph Neural Networks
Huihui Liu, Yiding Yang, Xinchao Wang
Catastrophic forgetting refers to the tendency that a neural network "forgets" the previous learned knowledge upon learning new tasks. Prior methods have been focused on overcoming…
Learning Propagation Rules for Attribution Map Generation
Yiding Yang, Jiayan Qiu, Mingli Song +2
Prior gradient-based attribution-map methods rely on handcrafted propagation rules for the non-linear/activation layers during the backward pass, so as to produce gradients of the…
Factorizable Graph Convolutional Networks
Yiding Yang, Zunlei Feng, Mingli Song +1
Graphs have been widely adopted to denote structural connections between entities. The relations are in many cases heterogeneous, but entangled together and denoted merely as a sin…
Distilling Knowledge from Graph Convolutional Networks
Yiding Yang, Jiayan Qiu, Mingli Song +2
Existing knowledge distillation methods focus on convolutional neural networks (CNNs), where the input samples like images lie in a grid domain, and have largely overlooked graph c…
Dual Teaching: A Practical Semi-supervised Wrapper Method
Fuqaing Liu, Chenwei Deng, Fukun Bi +1
Semi-supervised wrapper methods are concerned with building effective supervised classifiers from partially labeled data. Though previous works have succeeded in some fields, it is…
Boost Picking: A Universal Method on Converting Supervised Classification to Semi-supervised Classification
Fuqiang Liu, Fukun Bi, Yiding Yang +1
This paper proposes a universal method, Boost Picking, to train supervised classification models mainly by un-labeled data. Boost Picking only adopts two weak classifiers to estima…