Baichen Liu - RankMe

19 papers A* 2Journal 12Unranked 5

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	CRISP: Contrastive Residual Injection and Semantic Prompting for Continual Video Instance Segmentation. IEEE Trans. Image Process. Baichen Liu, Qi Lyu, Xudong Wang, Jiahua Dong, Lianqing Liu, Zhi Han
2026	J	`jnl`	Lifelong Embodied Navigation Learning. CoRR Xudong Wang, Jiahua Dong, Baichen Liu, Qi Lyu, Lianqing Liu, Zhi Han
2026	A*	`conf`	Lifelong Language-Conditioned Robotic Manipulation Learning. AAAI Xudong Wang, Zebin Han, Zhiyu Liu, Gan Li, Jiahua Dong, Baichen Liu, Lianqing Liu, Zhi Han
2026	J	`jnl`	Lifelong Language-Conditioned Robotic Manipulation Learning. CoRR Xudong Wang, Zebin Han, Zhiyu Liu, Gan Li, Jiahua Dong, Baichen Liu, Lianqing Liu, Zhi Han
2026	A*	`conf`	SeqWalker: Sequential-Horizon Vision-and-Language Navigation with Hierarchical Planning. AAAI Zebin Han, Xudong Wang, Baichen Liu, Qi Lyu, Zhenduo Shang, Jiahua Dong, Lianqing Liu, Zhi Han
2026	J	`jnl`	SeqWalker: Sequential-Horizon Vision-and-Language Navigation with Hierarchical Planning. CoRR Zebin Han, Xudong Wang, Baichen Liu, Qi Lyu, Zhenduo Shang, Jiahua Dong, Lianqing Liu, Zhi Han
2025	J	`jnl`	CRISP: Contrastive Residual Injection and Semantic Prompting for Continual Video Instance Segmentation. CoRR Baichen Liu, Qi Lyu, Xudong Wang, Jiahua Dong, Lianqing Liu, Zhi Han
2025	J	`jnl`	Rope-net: deep convolutional neural network via robust principal component analysis. Mach. Learn. Baichen Liu, Zhi Han, Xi'ai Chen, Yanmei Wang, Yandong Tang
2024	J	`jnl`	Developing Deep LSTMs With Later Temporal Attention for Predicting COVID-19 Severity, Clinical Outcome, and Antibody Level by Screening Serological Indicators Over Time. IEEE J. Biomed. Health Informatics Jiaxin Cai, Yang Li, Baichen Liu, Zhixi Wu, Shengjun Zhu, Qiliang Chen, Qing Lei, Hongyan Hou, Zhibin Guo, Hewei Jiang, Shujuan Guo, Feng Wang, Shengjing Huang, Shunzhi Zhu, Xionglin Fan, Sheng-ce Tao
2024	—	`conf`	Siamese Visual Tracking with Feature Enhancement Fusion. ICCAI Zhixi Wu, Baichen Liu, Shunzhi Zhu
2024	—	`conf`	VATBoost-Net: Integrating Enhanced Feature Perturbation and Detail Enhancement for Medical Image Segmentation. PRCV (14) Baichen Liu, Jiaxin Cai, Shunzhi Zhu
2023	J	`jnl`	DILS: depth incremental learning strategy. Frontiers Neurorobotics Yanmei Wang, Zhi Han, Siquan Yu, Shaojie Zhang, Baichen Liu, Huijie Fan
2023	J	`jnl`	Deep Convolutional Neural Networks with Zero-Padding: Feature Extraction and Learning. CoRR Zhi Han, Baichen Liu, Shao-Bo Lin, Ding-Xuan Zhou
2023	—	`conf`	Pathologic Myopia Classification based on Shuffle-EfficientNet Model. ICCPR Baichen Liu, Zhixi Wu, Shunzhi Zhu
2022	J	`jnl`	A novel compact design of convolutional layers with spatial transformation towards lower-rank representation for image classification. Knowl. Based Syst. Baichen Liu, Zhi Han, Xiai Chen, Wenming Shao, Huidi Jia, Yanmei Wang, Yandong Tang
2022	—	`conf`	An Analysis of Low-Rank Decomposition Selection for Deep Convolutional Neural Networks. ICIRA (2) Baichen Liu, Huidi Jia, Zhi Han, Xiai Chen, Yandong Tang
2022	—	`conf`	Group Sparsity Regularized High Order Tensor for HSIs Super-Resolution. ICIRA (4) Xiai Chen, Siyu Guo, Huidi Jia, Baichen Liu, Zhenyu Li, Zhi Han
2022	J	`jnl`	Nonconvex Nonlocal Tucker Decomposition for 3D Medical Image Super-Resolution. Frontiers Neuroinformatics Huidi Jia, Xiai Chen, Zhi Han, Baichen Liu, Tianhui Wen, Yandong Tang
2021	J	`jnl`	Low-rank decomposition on transformed feature maps domain for image denoising. Vis. Comput. Qiong Luo, Baichen Liu, Yang Zhang, Zhi Han, Yandong Tang

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.