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Chenyi Zi

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4 papers

2 author rows

TMLR Journal 2025 Journal Article

Let Your Light Shine: Foreground Portrait Matting via Deep Flash Priors

Tianyi Xiang
Yangyang Xu
Qingxuan Hu
Chenyi Zi
Nanxuan Zhao
Junle Wang
Shengfeng He

In this paper, we delve into a new perspective to solve image matting by revealing the foreground with flash priors. Previous Background Matting frameworks require a clean background as input, and although demonstrated powerfully, they are not practical to handle real-world scenarios with dynamic camera or background movement. We introduce the flash/no-flash image pair to portray the foreground object while eliminating the influence of dynamic background. The rationale behind this is that the foreground object is closer to the camera and thus received more light than the background. We propose a cascaded end-to-end network to integrate flash prior knowledge into the alpha matte estimation process. Particularly, a transformer-based Foreground Correlation Module is presented to connect foregrounds exposed in different lightings, which can effectively filter out the perturbation from the dynamic background and also robust to foreground motion. The initial prediction is concatenated with a Boundary Matting Network to polish the details of previous predictions. To supplement the training and evaluation of our flash/no-flash framework, we construct the first flash/no-flash portrait image matting dataset with 3,025 well-annotated alpha mattes. Experimental evaluations show that our proposed model significantly outperforms existing trimap-free matting methods on scenes with dynamic backgrounds. Moreover, we detailedly discuss and analyze the effects of different prior knowledge on static and dynamic backgrounds. In contrast to the restricted scenarios of Background Matting, we demonstrate a flexible and reliable solution in real-world cases with the camera or background movements.

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ICLR Conference 2024 Conference Paper

Deep Reinforcement Learning for Modelling Protein Complexes

Ziqi Gao
Tao Feng
Jiaxuan You
Chenyi Zi
Yan Zhou
Chen Zhang
Jia Li 0009

Structure prediction of large protein complexes (a.k.a., protein multimer mod- elling, PMM) can be achieved through the one-by-one assembly using provided dimer structures and predicted docking paths. However, existing PMM methods struggle with vast search spaces and generalization challenges: (1) The assembly of a N -chain multimer can be depicted using graph structured data, with each chain represented as a node and assembly actions as edges. Thus the assembly graph can be arbitrary acyclic undirected connected graph, leading to the com- binatorial optimization space of N^(N −2) for the PMM problem. (2) Knowledge transfer in the PMM task is non-trivial. The gradually limited data availability as the chain number increases necessitates PMM models that can generalize across multimers of various chains. To address these challenges, we propose GAPN, a Generative Adversarial Policy Network powered by domain-specific rewards and adversarial loss through policy gradient for automatic PMM prediction. Specifi- cally, GAPN learns to efficiently search through the immense assembly space and optimize the direct docking reward through policy gradient. Importantly, we de- sign a adversarial reward function to enhance the receptive field of our model. In this way, GAPN will simultaneously focus on a specific batch of multimers and the global assembly rules learned from multimers with varying chain numbers. Empirically, we have achieved both significant accuracy (measured by RMSD and TM-Score) and efficiency improvements compared to leading complex mod- eling software. GAPN outperforms the state-of-the-art method (MoLPC) with up to 27% improvement in TM-Score, with a speed-up of 600×.

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NeurIPS Conference 2024 Conference Paper

ProG: A Graph Prompt Learning Benchmark

Chenyi Zi
Haihong Zhao
Xiangguo Sun
Yiqing Lin
Hong Cheng
Jia Li

Artificial general intelligence on graphs has shown significant advancements across various applications, yet the traditional `Pre-train & Fine-tune' paradigm faces inefficiencies and negative transfer issues, particularly in complex and few-shot settings. Graph prompt learning emerges as a promising alternative, leveraging lightweight prompts to manipulate data and fill the task gap by reformulating downstream tasks to the pretext. However, several critical challenges still remain: how to unify diverse graph prompt models, how to evaluate the quality of graph prompts, and to improve their usability for practical comparisons and selection. In response to these challenges, we introduce the first comprehensive benchmark for graph prompt learning. Our benchmark integrates SIX pre-training methods and FIVE state-of-the-art graph prompt techniques, evaluated across FIFTEEN diverse datasets to assess performance, flexibility, and efficiency. We also present 'ProG', an easy-to-use open-source library that streamlines the execution of various graph prompt models, facilitating objective evaluations. Additionally, we propose a unified framework that categorizes existing graph prompt methods into two main approaches: prompts as graphs and prompts as tokens. This framework enhances the applicability and comparison of graph prompt techniques. The code is available at: https: //github. com/sheldonresearch/ProG.

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NeurIPS Conference 2024 Conference Paper

UniGAD: Unifying Multi-level Graph Anomaly Detection

Yiqing Lin
Jianheng Tang
Chenyi Zi
H. Vicky Zhao
Yuan Yao
Jia Li

Graph Anomaly Detection (GAD) aims to identify uncommon, deviated, or suspicious objects within graph-structured data. Existing methods generally focus on a single graph object type (node, edge, graph, etc. ) and often overlook the inherent connections among different object types of graph anomalies. For instance, a money laundering transaction might involve an abnormal account and the broader community it interacts with. To address this, we present UniGAD, the first unified framework for detecting anomalies at node, edge, and graph levels jointly. Specifically, we develop the Maximum Rayleigh Quotient Subgraph Sampler (MRQSampler) that unifies multi-level formats by transferring objects at each level into graph-level tasks on subgraphs. We theoretically prove that MRQSampler maximizes the accumulated spectral energy of subgraphs (i. e. , the Rayleigh quotient) to preserve the most significant anomaly information. To further unify multi-level training, we introduce a novel GraphStitch Network to integrate information across different levels, adjust the amount of sharing required at each level, and harmonize conflicting training goals. Comprehensive experiments show that UniGAD outperforms both existing GAD methods specialized for a single task and graph prompt-based approaches for multiple tasks, while also providing robust zero-shot task transferability.

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