Publications (14)
MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving: A Review
Zhiqing Wei, Fengkai Zhang, Shuo Chang +3
With autonomous driving developing in a booming stage, accurate object detection in complex scenarios attract wide attention to ensure the safety of autonomous driving. Millimeter…
LoopFM: Learning frOm HistOrical RePresentations of Foundation Model for Recommendation
Shali Jiang, Hua Zheng, Boyang Liu +40
Knowledge distillation (KD) transfers a single scalar prediction from a large foundation model (FM) to compact vertical models (VMs), suffering from diminishing transfer ratio -- t…
Exemplar Loss for Siamese Network in Visual Tracking
Shuo Chang, YiFan Zhang, Sai Huang +2
Visual tracking plays an important role in perception system, which is a crucial part of intelligent transportation. Recently, Siamese network is a hot topic for visual tracking to…
IE-SRGS: An Internal-External Knowledge Fusion Framework for High-Fidelity 3D Gaussian Splatting Super-Resolution
Xiang Feng, Tieshi Zhong, Shuo Chang +7
Reconstructing high-resolution (HR) 3D Gaussian Splatting (3DGS) models from low-resolution (LR) inputs remains challenging due to the lack of fine-grained textures and geometry. E…
CSRD2025: A Large-Scale Synthetic Radio Dataset for Spectrum Sensing in Wireless Communications
Shuo Chang, Rui Sun, Jiashuo He +3
The development of Large AI Models (LAMs) for wireless communications, particularly for complex tasks like spectrum sensing, is critically dependent on the availability of vast, di…
Channel-robust Automatic Modulation Classification Using Spectral Quotient Cumulants
Sai Huang, Yuting Chen, Jiashuo He +2
Automatic modulation classification (AMC) is to identify the modulation format of the received signal corrupted by the channel effects and noise. Most existing works focus on the i…
A Large Scale Benchmark for Test Time Adaptation Methods in Medical Image Segmentation
Wenjing Yu, Shuo Jiang, Yifei Chen +9
Test time Adaptation is a promising approach for mitigating domain shift in medical image segmentation; however, current evaluations remain limited in terms of modality coverage, t…
Mixture of Sequence: Theme-Aware Mixture-of-Experts for Long-Sequence Recommendation
Xiao Lin, Zhicheng Tang, Weilin Cong +14
Sequential recommendation has rapidly advanced in click-through rate prediction due to its ability to model dynamic user interests. A key challenge, however, lies in modeling long…
Deep Learning based Cross-Receiver Radio Frequency Fingerprint Identification Under Varying Channels
Jiashuo He, Yumeng Wang, Feiyang He +4
Radio frequency fingerprint identification (RFFI) exploits device-specific hardware impairments for transmitter recognition, but its performance is highly vulnerable to receiver va…
Kunlun: Establishing Scaling Laws for Massive-Scale Recommendation Systems through Unified Architecture Design
Bojian Hou, Xiaolong Liu, Xiaoyi Liu +26
Deriving predictable scaling laws that govern the relationship between model performance and computational investment is crucial for designing and allocating resources in massive-s…
Joint Signal Detection and Automatic Modulation Classification via Deep Learning
Huijun Xing, Xuhui Zhang, Shuo Chang +4
Signal detection and modulation classification are two crucial tasks in various wireless communication systems. Different from prior works that investigate them independently, this…
External Large Foundation Model: How to Efficiently Serve Trillions of Parameters for Online Ads Recommendation
Mingfu Liang, Xi Liu, Rong Jin +104
Ads recommendation is a prominent service of online advertising systems and has been actively studied. Recent studies indicate that scaling-up and advanced design of the recommenda…
Toward Robust Early Detection of Alzheimer's Disease via an Integrated Multimodal Learning Approach
Yifei Chen, Shenghao Zhu, Zhaojie Fang +9
Alzheimer's Disease (AD) is a complex neurodegenerative disorder marked by memory loss, executive dysfunction, and personality changes. Early diagnosis is challenging due to subtle…
Learning from Negative User Feedback and Measuring Responsiveness for Sequential Recommenders
Yueqi Wang, Yoni Halpern, Shuo Chang +9
Sequential recommenders have been widely used in industry due to their strength in modeling user preferences. While these models excel at learning a user's positive interests, less…