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Victor C. M. Leung

9 accepted papers

2026

M3SR: Multi-Scale Multi-Perceptual Mamba for Efficient Spectral Reconstruction

AAAI 2026technical

The Mamba architecture has been widely applied to various low-level vision tasks due to its exceptional adaptability and strong performance. Although the Mamba architecture has been adopted for spectral reconstruction, it still faces the following two challenges: (1) Single spatial perception limits

Cited by 0SourcePDFScholar
2025

Privacy-Aware Federated Fine-Tuning of Large Pretrained Models With Just Forward Propagation

ICASSP 2025accepted

With the extraordinary success of generative artificial intelligence, large pretrained models (LPMs) have been widely used to achieve human-level performance. Despite the one-shot capability, it is always preferred to fine-tune the LPMs for domain-specific downstream tasks. Therefore, the federated…

Cited by 0SourceScholar
2025

StereoMamba: Enhancing Stereo Image Super-Resolution with Structured State Space Models and Bi-Directional Cross Attention

ICASSP 2025accepted

Stereo image super-resolution (SR) aims to enhance image resolution by leveraging the complementary information from stereo image pairs. While convolutional neural network (CNN)-based methods have traditionally dominated this field, they struggle with capturing long-range dependencies. Transformer-b…

Cited by 0SourceScholar
2025

Unsupervised Histopathological Image Semantic Segmentation with Overlapping Patches Consistency Constraint

ICCV 2025poster

Massive requirement for pixel-wise annotations in histopathological image segmentation poses a significant challenge, leading to increasing interest in Unsupervised Semantic Segmentation (USS) as a viable alternative. Pre-trained model-based methods have been widely used in USS, achieving promising…

Cited by 0SourcePDFScholar
2024

Multi-Modal GPT-4 Aided Action Planning and Reasoning for Self-driving Vehicles

ICASSP 2024accepted

Explainable decision-making is critical for building trust in autonomous vehicles. We investigate the use of a pre-trained large language model (LLM) to derive comprehensible driving decisions from multi-modal time-series data captured by a monocular camera on an autonomous vehicle. Leveraging a gra…

Cited by 0SourceScholar
2023

Federated Semi-Supervised Learning for Object Detection in Autonomous Driving

ICASSP 2023accepted

One of the main challenges in designing deep learning networks for autonomous driving is the lack of labeled data. Recent trends that address this problem involve the use of unlabeled data. In this paper, we propose a unified semi-supervised and federated learning (FL) approach that is designed to o…

Cited by 0SourceScholar
2022

An Online Throughput Maximization Algorithm for Green Coordinated Multi-Point Systems

ICASSP 2022accepted

Wireless systems are upgraded to use green energy (e.g., solar, wind, and tide energy) such that the greenhouse gas emission can be neutralized. This work incorporates the on-grid energy into a green coordinated multi-point (CoMP) system to handle the volatile arrival of green energy. In the green C…

Cited by 0SourceScholar
2020

Latency-Minimized Design of secure transmissions in UAV-Aided Communications

ICASSP 2020accepted

Unmanned aerial vehicles (UAVs) can be utilized as aerial base stations to provide communication service for remote mobile users due to their high mobility and flexible deployment. However, the line-of-sight (LoS) wireless links are vulnerable to be intercepted by the eavesdropper (Eve), which prese…

Cited by 0SourceScholar
2019

Latency Driven Fronthaul Bandwidth Allocation and Cooperative Beamforming for Cache-enabled Cloud-based Small Cell Networks

ICASSP 2019accepted

This paper considers content delivery of the cache-enabled small cell networks (C-SCNs), where users with the same request form a multicast group and are served by a cluster of small-cell base stations (SBSs) under the coordination of the central processor. The performance of such a coordination is…

Cited by 0SourceScholar