3 citations · 4 across the 12 of their papers we have counts for
15 papers
AsyncCouple-Flow: Asynchronous Cross-Modal Coupling and Flow Matching for Spatio-Temporal Forecasting
Zhixiang Wu, Yining Liu, Bo Zhao +4
Multi-modal spatio-temporal forecasting (MM-STF) supports weather nowcasting, traffic prediction, and earth-system modeling by combining heterogeneous sources such as physical fiel…
Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning
Yuqi Li, Yuedong Tan, Huiran Duan +7
Unified multimodal object tracking has achieved remarkable robustness by leveraging complementary sensor data (e.g., RGB, Thermal, Depth), yet the heavy computational burden of sta…
GaitKD: A Universal Decoupled Distillation Framework for Efficient Gait Recognition
Yuqi Li, Qian Zhou, Huiran Duan +5
Gait recognition is an attractive biometric modality for long-range and contact-free identification, but high-performing gait models often rely on deep and computationally expensiv…
Distilling Time Series Foundation Models for Efficient Forecasting
Yuqi Li, Kuiye Ding, Chuanguang Yang +2
Time Series foundation models (TSFMs) deliver strong forecasting performance through large-scale pretraining, but their large parameter sizes make deployment costly. While knowledg…
MMT-ARD: Multimodal Multi-Teacher Adversarial Distillation for Robust Vision-Language Models
Yuqi Li, Junhao Dong, Chuanguang Yang +5
Vision-Language Models (VLMs) are increasingly deployed in safety-critical applications, making their adversarial robustness a crucial concern. While adversarial knowledge distilla…
DDTime: Dataset Distillation with Spectral Alignment and Information Bottleneck for Time-Series Forecasting
Yuqi Li, Kuiye Ding, Chuanguang Yang +5
Time-series forecasting is fundamental across many domains, yet training accurate models often requires large-scale datasets and substantial computational resources. Dataset distil…
Asymmetric Cross-Modal Knowledge Distillation: Bridging Modalities with Weak Semantic Consistency
Riling Wei, Kelu Yao, Chuanguang Yang +3
Cross-modal Knowledge Distillation has demonstrated promising performance on paired modalities with strong semantic connections, referred to as Symmetric Cross-modal Knowledge Dist…
AMMKD: Adaptive Multimodal Multi-teacher Distillation for Lightweight Vision-Language Models
Yuqi Li, Chuanguang Yang, Junhao Dong +6
The success of large-scale visual language pretraining (VLP) models has driven widespread adoption of image-text retrieval tasks. However, their deployment on mobile devices remain…
Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting
Yuqi Li, Chuanguang Yang, Hansheng Zeng +5
Spatiotemporal forecasting tasks, such as traffic flow, combustion dynamics, and weather forecasting, often require complex models that suffer from low training efficiency and high…
MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation
Weilun Feng, Chuanguang Yang, Haotong Qin +10
Diffusion models have demonstrated remarkable performance on vision generation tasks. However, the high computational complexity hinders its wide application on edge devices. Quant…
SepPrune: Structured Pruning for Efficient Deep Speech Separation
Yuqi Li, Kai Li, Xin Yin +6
Although deep learning has substantially advanced speech separation in recent years, most existing studies continue to prioritize separation quality while overlooking computational…
Enhancing Image Generation Fidelity via Progressive Prompts
Zhen Xiong, Yuqi Li, Chuanguang Yang +4
The diffusion transformer (DiT) architecture has attracted significant attention in image generation, achieving better fidelity, performance, and diversity. However, most existing…
Federated Knowledge Distillation for Multi-Model Architectures Lithography Hotspot Detection
Yuqi Li, Xingyou Lin, Yanli Li +6
As a special type of multimedia data, Lithography Hotspot Detection (LHD) training often requires stronger privacy protection than conventional multimedia data, and federated learn…
SGLP: A Similarity Guided Fast Layer Partition Pruning for Compressing Large Deep Models
Yuqi Li, Yao Lu, Junhao Dong +7
Layer pruning has emerged as a potent approach to remove redundant layers in the pre-trained network on the purpose of reducing network size and improve computational efficiency. H…
Prototype-Driven Multi-Feature Generation for Visible-Infrared Person Re-identification
Jiarui Li, Zhen Qiu, Yilin Yang +3
The primary challenges in visible-infrared person re-identification arise from the differences between visible (vis) and infrared (ir) images, including inter-modal and intra-modal…