9 citations · 25 across the 21 of their papers we have counts for
22 papers
RSICCLLM: A Multimodal Large Language Model for Remote Sensing Image Change Captioning
Yelin Wang, Zijia Song, Shuo Ye +6
Remote Sensing Image Change Captioning (RSICC) aims to describe changes between bi-temporal remote sensing images and holds significant research and application value. However, mos…
DFM: Difference Feature Modeling with Text-Guided Gated Contrastive Loss for Remote Sensing Image Change Captioning
Yelin Wang, Zijia Song, Chuanguang Yang +4
The primary goal of Remote Sensing Image Change Captioning (RSICC) is to automatically generate descriptions of changes between remote sensing images captured at different time poi…
Quantized Visual Geometry Grounded Transformer
Weilun Feng, Haotong Qin, Mingqiang Wu +8
Learning-based 3D reconstruction models, represented by Visual Geometry Grounded Transformers (VGGTs), have made remarkable progress with the use of large-scale transformers. Their…
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…
SRKD: Towards Efficient 3D Point Cloud Segmentation via Structure- and Relation-aware Knowledge Distillation
Yuqi Li, Junhao Dong, Zeyu Dong +3
3D point cloud segmentation faces practical challenges due to the computational complexity and deployment limitations of large-scale transformer-based models. To address this, we p…
SifterNet: A Generalized and Model-Agnostic Trigger Purification Approach
Shaoye Luo, Xinxin Fan, Quanliang Jing +4
Aiming at resisting backdoor attacks in convolution neural networks and vision Transformer-based large model, this paper proposes a generalized and model-agnostic trigger-purificat…
Multi-party Collaborative Attention Control for Image Customization
Han Yang, Chuanguang Yang, Qiuli Wang +4
The rapid advancement of diffusion models has increased the need for customized image generation. However, current customization methods face several limitations: 1) typically acce…
Multi-Teacher Knowledge Distillation with Reinforcement Learning for Visual Recognition
Chuanguang Yang, Xinqiang Yu, Han Yang +4
Multi-teacher Knowledge Distillation (KD) transfers diverse knowledge from a teacher pool to a student network. The core problem of multi-teacher KD is how to balance distillation…
Continual Learning in the Frequency Domain
Ruiqi Liu, Boyu Diao, Libo Huang +3
Continual learning (CL) is designed to learn new tasks while preserving existing knowledge. Replaying samples from earlier tasks has proven to be an effective method to mitigate th…
Relational Diffusion Distillation for Efficient Image Generation
Weilun Feng, Chuanguang Yang, Zhulin An +4
Although the diffusion model has achieved remarkable performance in the field of image generation, its high inference delay hinders its wide application in edge devices with scarce…
Unified Dual-Intent Translation for Joint Modeling of Search and Recommendation
Yuting Zhang, Yiqing Wu, Ruidong Han +7
Recommendation systems, which assist users in discovering their preferred items among numerous options, have served billions of users across various online platforms. Intuitively,…
Online Policy Distillation with Decision-Attention
Xinqiang Yu, Chuanguang Yang, Chengqing Yu +3
Policy Distillation (PD) has become an effective method to improve deep reinforcement learning tasks. The core idea of PD is to distill policy knowledge from a teacher agent to a s…
IOR: Inversed Objects Replay for Incremental Object Detection
Zijia An, Boyu Diao, Libo Huang +3
Existing Incremental Object Detection (IOD) methods partially alleviate catastrophic forgetting when incrementally detecting new objects in real-world scenarios. However, many of t…
Exemplar-Free Class Incremental Learning via Incremental Representation
Libo Huang, Zhulin An, Yan Zeng +3
Exemplar-Free Class Incremental Learning (efCIL) aims to continuously incorporate the knowledge from new classes while retaining previously learned information, without storing any…
CLIP-KD: An Empirical Study of CLIP Model Distillation
Chuanguang Yang, Zhulin An, Libo Huang +5
Contrastive Language-Image Pre-training (CLIP) has become a promising language-supervised visual pre-training framework. This paper aims to distill small CLIP models supervised by…
Categories of Response-Based, Feature-Based, and Relation-Based Knowledge Distillation
Chuanguang Yang, Xinqiang Yu, Zhulin An +1
Deep neural networks have achieved remarkable performance for artificial intelligence tasks. The success behind intelligent systems often relies on large-scale models with high com…
Modeling Dual Period-Varying Preferences for Takeaway Recommendation
Yuting Zhang, Yiqing Wu, Ran Le +7
Takeaway recommender systems, which aim to accurately provide stores that offer foods meeting users' interests, have served billions of users in our daily life. Different from trad…
eTag: Class-Incremental Learning with Embedding Distillation and Task-Oriented Generation
Libo Huang, Yan Zeng, Chuanguang Yang +3
Class-Incremental Learning (CIL) aims to solve the neural networks' catastrophic forgetting problem, which refers to the fact that once the network updates on a new task, its perfo…
MixSKD: Self-Knowledge Distillation from Mixup for Image Recognition
Chuanguang Yang, Zhulin An, Helong Zhou +5
Unlike the conventional Knowledge Distillation (KD), Self-KD allows a network to learn knowledge from itself without any guidance from extra networks. This paper proposes to perfor…
Online Knowledge Distillation via Mutual Contrastive Learning for Visual Recognition
Chuanguang Yang, Zhulin An, Helong Zhou +3
The teacher-free online Knowledge Distillation (KD) aims to train an ensemble of multiple student models collaboratively and distill knowledge from each other. Although existing on…
Localizing Semantic Patches for Accelerating Image Classification
Chuanguang Yang, Zhulin An, Yongjun Xu
Existing works often focus on reducing the architecture redundancy for accelerating image classification but ignore the spatial redundancy of the input image. This paper proposes a…