3 citations · 6 across the 20 of their papers we have counts for
9 papers · 1 filter
ContextFusion and Bootstrap: An Effective Approach to Improve Slot Attention-Based Object-Centric Learning
Pinzhuo Tian, Shengjie Yang, Hang Yu +1
A key human ability is to decompose a scene into distinct objects and use their relationships to understand the environment. Object-centric learning aims to mimic this process in a…
Temporal Unlearnable Examples: Preventing Personal Video Data from Unauthorized Exploitation by Object Tracking
Qiangqiang Wu, Yi Yu, Chenqi Kong +5
With the rise of social media, vast amounts of user-uploaded videos (e.g., YouTube) are utilized as training data for Visual Object Tracking (VOT). However, the VOT community has l…
MTL-UE: Learning to Learn Nothing for Multi-Task Learning
Yi Yu, Song Xia, Siyuan Yang +5
Most existing unlearnable strategies focus on preventing unauthorized users from training single-task learning (STL) models with personal data. Nevertheless, the paradigm has recen…
Open-set Anomaly Segmentation in Complex Scenarios
Song Xia, Yi Yu, Henghui Ding +4
Precise segmentation of out-of-distribution (OoD) objects, herein referred to as anomalies, is crucial for the reliable deployment of semantic segmentation models in open-set, safe…
Digital Staining with Knowledge Distillation: A Unified Framework for Unpaired and Paired-But-Misaligned Data
Ziwang Xu, Lanqing Guo, Satoshi Tsutsui +3
Staining is essential in cell imaging and medical diagnostics but poses significant challenges, including high cost, time consumption, labor intensity, and irreversible tissue alte…
Theoretical Insights in Model Inversion Robustness and Conditional Entropy Maximization for Collaborative Inference Systems
Song Xia, Yi Yu, Wenhan Yang +5
By locally encoding raw data into intermediate features, collaborative inference enables end users to leverage powerful deep learning models without exposure of sensitive raw data…
Reconciling Stochastic and Deterministic Strategies for Zero-shot Image Restoration using Diffusion Model in Dual
Chong Wang, Lanqing Guo, Zixuan Fu +4
Plug-and-play (PnP) methods offer an iterative strategy for solving image restoration (IR) problems in a zero-shot manner, using a learned \textit{discriminative denoiser} as the i…
Temporal-Guided Spiking Neural Networks for Event-Based Human Action Recognition
Siyuan Yang, Shilin Lu, Shizheng Wang +3
This paper explores the promising interplay between spiking neural networks (SNNs) and event-based cameras for privacy-preserving human action recognition (HAR). The unique feature…
Class-Independent Increment: An Efficient Approach for Multi-label Class-Incremental Learning
Chenhao Ding, Songlin Dong, Zhengdong Zhou +4
Current research on class-incremental learning primarily focuses on single-label classification tasks. However, real-world applications often involve multi-label scenarios, such as…