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pro vyhledávání: '"Liu, HongBin"'
Monocular depth estimation has shown promise in general imaging tasks, aiding in localization and 3D reconstruction. While effective in various domains, its application to bronchoscopic images is hindered by the lack of labeled data, challenging the
Externí odkaz:
http://arxiv.org/abs/2411.04404
Accurate and complete segmentation of airways in chest CT images is essential for the quantitative assessment of lung diseases and the facilitation of pulmonary interventional procedures. Although deep learning has led to significant advancements in
Externí odkaz:
http://arxiv.org/abs/2410.18456
In a prompt injection attack, an attacker injects a prompt into the original one, aiming to make the LLM follow the injected prompt and perform a task chosen by the attacker. Existing prompt injection attacks primarily focus on how to blend the injec
Externí odkaz:
http://arxiv.org/abs/2410.14827
Visual hallucination (VH) occurs when a multimodal large language model (MLLM) generates responses with incorrect visual details for prompts. Existing methods for generating VH test cases primarily rely on human annotations, typically in the form of
Externí odkaz:
http://arxiv.org/abs/2410.11242
Autor:
Liu, Hongbin, Chen, Youzheng, Narayanan, Arun, Balachandran, Athula, Moreno, Pedro J., Wang, Lun
Recent advances in text-to-speech (TTS) systems, particularly those with voice cloning capabilities, have made voice impersonation readily accessible, raising ethical and legal concerns due to potential misuse for malicious activities like misinforma
Externí odkaz:
http://arxiv.org/abs/2410.06572
Large ASR models can inadvertently leak sensitive information, which can be mitigated by formal privacy measures like differential privacy (DP). However, traditional DP training is computationally expensive, and can hurt model performance. Our study
Externí odkaz:
http://arxiv.org/abs/2410.01948
Autor:
Chen, Zhen, Luo, Xingjian, Wu, Jinlin, Bai, Long, Lei, Zhen, Ren, Hongliang, Ourselin, Sebastien, Liu, Hongbin
Surgical phase recognition is critical for assisting surgeons in understanding surgical videos. Existing studies focused more on online surgical phase recognition, by leveraging preceding frames to predict the current frame. Despite great progress, t
Externí odkaz:
http://arxiv.org/abs/2409.12467
Autor:
Mörchen, Maximilian, Low, Guang Hao, Weymuth, Thomas, Liu, Hongbin, Troyer, Matthias, Reiher, Markus
Quantum computation for chemical problems will require the construction of guiding states with sufficient overlap with a target state. Since easily available and initializable mean-field states are characterized by an overlap that is reduced for mult
Externí odkaz:
http://arxiv.org/abs/2409.08910
Autor:
van Dam, Wim, Liu, Hongbin, Low, Guang Hao, Paetznick, Adam, Paz, Andres, Silva, Marcus, Sundaram, Aarthi, Svore, Krysta, Troyer, Matthias
We demonstrate the first end-to-end integration of high-performance computing (HPC), reliable quantum computing, and AI in a case study on catalytic reactions producing chiral molecules. We present a hybrid computation workflow to determine the stron
Externí odkaz:
http://arxiv.org/abs/2409.05835
Autor:
Tian, Qingyao, Chen, Zhen, Liao, Huai, Huang, Xinyan, Li, Lujie, Ourselin, Sebastien, Liu, Hongbin
Single-image depth estimation is essential for endoscopy tasks such as localization, reconstruction, and augmented reality. Most existing methods in surgical scenes focus on in-domain depth estimation, limiting their real-world applicability. This co
Externí odkaz:
http://arxiv.org/abs/2409.05442