3 citations · 7 across the 12 of their papers we have counts for
13 papers
KVE-KD: Key Visual Evidence-Guided Knowledge Distillation for Vision-Language Models
Jianbin Zhang, Xin Sun, Shanwen Wang +2
Knowledge distillation is crucial for deploying vision-language models on resource-constrained devices. However, existing methods typically impose uniform supervision across visual…
Decoder Gradient Shields: A Family of Provable and High-Fidelity Methods Against Gradient-Based Box-Free Watermark Removal
Haonan An, Guang Hua, Wei Du +5
Box-free model watermarking has gained significant attention in deep neural network (DNN) intellectual property protection due to its model-agnostic nature and its ability to flexi…
DiffPixelFormer: Differential Pixel-Aware Transformer for RGB-D Indoor Scene Segmentation
Yan Gong, Jianli Lu, Yongsheng Gao +3
Indoor semantic segmentation is fundamental to computer vision and robotics, supporting applications such as autonomous navigation, augmented reality, and smart environments. Altho…
MEDTalk: Multimodal Controlled 3D Facial Animation with Dynamic Emotions by Disentangled Embedding
Chang Liu, Ye Pan, Chenyang Ding +2
Audio-driven emotional 3D facial animation aims to generate synchronized lip movements and vivid facial expressions. However, most existing approaches focus on static and predefine…
Progressive Bird's Eye View Perception for Safety-Critical Autonomous Driving: A Comprehensive Survey
Yan Gong, Naibang Wang, Jianli Lu +13
Bird's-Eye-View (BEV) perception has become a foundational paradigm in autonomous driving, enabling unified spatial representations that support robust multi-sensor fusion and mult…
Bootstrap Deep Spectral Clustering with Optimal Transport
Wengang Guo, Wei Ye, Chunchun Chen +4
Spectral clustering is a leading clustering method. Two of its major shortcomings are the disjoint optimization process and the limited representation capacity. To address these is…
Removing Box-Free Watermarks for Image-to-Image Models via Query-Based Reverse Engineering
Haonan An, Guang Hua, Hangcheng Cao +4
The intellectual property of deep generative networks (GNets) can be protected using a cascaded hiding network (HNet) which embeds watermarks (or marks) into GNet outputs, known as…
MAP: Revisiting Weight Decomposition for Low-Rank Adaptation
Chongjie Si, Zhiyi Shi, Yadao Wang +3
The rapid development of large language models has revolutionized natural language processing, but their fine-tuning remains computationally expensive, hindering broad deployment.…
QArtSR: Quantization via Reverse-Module and Timestep-Retraining in One-Step Diffusion based Image Super-Resolution
Libo Zhu, Haotong Qin, Kaicheng Yang +5
One-step diffusion-based image super-resolution (OSDSR) models are showing increasingly superior performance nowadays. However, although their denoising steps are reduced to one an…
Decoder Gradient Shield: Provable and High-Fidelity Prevention of Gradient-Based Box-Free Watermark Removal
Haonan An, Guang Hua, Zhengru Fang +3
The intellectual property of deep image-to-image models can be protected by the so-called box-free watermarking. It uses an encoder and a decoder, respectively, to embed into and e…
Weakly-supervised anomaly detection for multimodal data distributions
Xu Tan, Junqi Chen, Sylwan Rahardja +2
Weakly-supervised anomaly detection can outperform existing unsupervised methods with the assistance of a very small number of labeled anomalies, which attracts increasing attentio…
Joint Selective State Space Model and Detrending for Robust Time Series Anomaly Detection
Junqi Chen, Xu Tan, Sylwan Rahardja +2
Deep learning-based sequence models are extensively employed in Time Series Anomaly Detection (TSAD) tasks due to their effective sequential modeling capabilities. However, the abi…
MSS-PAE: Saving Autoencoder-based Outlier Detection from Unexpected Reconstruction
Xu Tan, Jiawei Yang, Junqi Chen +2
AutoEncoders (AEs) are commonly used for machine learning tasks due to their intrinsic learning ability. This unique characteristic can be capitalized for Outlier Detection (OD). H…