Qian-Wei Wang - RankMe

14 papers A* 6B 1Journal 7

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Explicit Uncertainty Modeling for Active CLIP Adaptation with Dual Prompt Tuning. CoRR Qian-Wei Wang, Yaguang Song, Shu-Tao Xia
2026	J	`jnl`	Fine-tuning Pre-trained Vision-Language Models in a Human-Annotation-Free Manner. CoRR Qian-Wei Wang, Guanghao Meng, Ren Cai, Yaguang Song, Shu-Tao Xia
2026	A*	`conf`	Imagine with Layout and Sketch: Enhancing Vision-Language Retrieval with Dual-Stream Multi-Modal Query Refinement. AAAI Guanghao Meng, Jinpeng Wang, Qian-Wei Wang, Xudong Ren, Dan Zhao
2025	A*	`conf`	Pre-Trained Vision-Language Models as Noisy Partial Annotators. AAAI Qian-Wei Wang, Yuqiu Xie, Letian Zhang, Zimo Liu, Shu-Tao Xia
2025	J	`jnl`	Pre-trained Vision-Language Models Assisted Noisy Partial Label Learning. CoRR Qian-Wei Wang, Yuqiu Xie, Letian Zhang, Zimo Liu, Shu-Tao Xia
2024	A*	`conf`	Controller-Guided Partial Label Consistency Regularization with Unlabeled Data. AAAI Qian-Wei Wang, Bowen Zhao, Mingyan Zhu, Tianxiang Li, Zimo Liu, Shu-Tao Xia
2024	J	`jnl`	Delving into Identify-Emphasize Paradigm for Combating Unknown Bias. Int. J. Comput. Vis. Bowen Zhao, Chen Chen, Qian-Wei Wang, Anfeng He, Shu-Tao Xia
2024	J	`jnl`	Pre-Trained Vision-Language Models as Partial Annotators. CoRR Qian-Wei Wang, Yuqiu Xie, Letian Zhang, Zimo Liu, Shu-Tao Xia
2023	A*	`conf`	Combating Unknown Bias with Effective Bias-Conflicting Scoring and Gradient Alignment. AAAI Bowen Zhao, Chen Chen, Qian-Wei Wang, Anfeng He, Shutao Xia
2023	J	`jnl`	Delving into Identify-Emphasize Paradigm for Combating Unknown Bias. CoRR Bowen Zhao, Chen Chen, Qian-Wei Wang, Anfeng He, Shu-Tao Xia
2023	B	`conf`	LKBQ: Pushing the Limit of Post-Training Quantization to Extreme 1 bit. ICIP Tianxiang Li, Bin Chen, Qian-Wei Wang, Yujun Huang, Shu-Tao Xia
2022	J	`jnl`	Towards Mitigating the Problem of Insufficient and Ambiguous Supervision in Online Crowdsourcing Annotation. CoRR Qian-Wei Wang, Bowen Zhao, Mingyan Zhu, Tianxiang Li, Zimo Liu, Shu-Tao Xia
2020	A*	`conf`	Learning from Weak-Label Data: A Deep Forest Expedition. AAAI Qian-Wei Wang, Liang Yang, Yufeng Li
2019	A*	`conf`	Partial Label Learning with Unlabeled Data. IJCAI Qian-Wei Wang, Yufeng Li, Zhi-Hua Zhou

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.