1 citations · 1 across the 10 of their papers we have counts for
12 papers
Relational Semantic Grounding for Test-Time Discovery
Tianle Liu, Gongbo Cheng, Chenggong Ni +4
Test-Time Discovery (TTD) aims to recognize known classes and detect novel ones from a continuous unlabeled test stream after deployment. Existing TTD methods mainly rely on visual…
Safety-Critical Quadrotor Navigation Using Fourth-Order Control Barrier Functions with Full Nonlinear Quadrotor Dynamics
Yanze Liu, Yaşar İdikut, Dhruv Madhusudan Agrawal +2
Safety-critical quadrotor navigation in cluttered environments typically relies on hierarchical architectures in which a high-level safety filter modifies position references that…
DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP
Kaile Du, Zihan Ye, Junzhou Xie +7
Multi-label class-incremental learning (MLCIL) continuously expands the label space while recognizing multiple co-occurring categories, making catastrophic forgetting a central cha…
Ambiguity-Guided Learnable Distribution Calibration for Semi-Supervised Few-Shot Class-Incremental Learning
Fan Lyu, Linglan Zhao, Chengyan Liu +5
Few-Shot Class-Incremental Learning (FSCIL) focuses on models learning new concepts from limited data while retaining knowledge of previous classes. Recently, many studies have sta…
Test-Time Discovery via Hashing Memory
Fan Lyu, Tianle Liu, Zhang Zhang +2
We introduce Test-Time Discovery (TTD) as a novel task that addresses class shifts during testing, requiring models to simultaneously identify emerging categories while preserving…
Conformal Uncertainty Indicator for Continual Test-Time Adaptation
Fan Lyu, Hanyu Zhao, Ziqi Shi +4
Continual Test-Time Adaptation (CTTA) aims to adapt models to sequentially changing domains during testing, relying on pseudo-labels for self-adaptation. However, incorrect pseudo-…
CALA: A Class-Aware Logit Adapter for Few-Shot Class-Incremental Learning
Chengyan Liu, Linglan Zhao, Fan Lyu +3
Few-Shot Class-Incremental Learning (FSCIL) defines a practical but challenging task where models are required to continuously learn novel concepts with only a few training samples…
Rebalancing Multi-Label Class-Incremental Learning
Kaile Du, Yifan Zhou, Fan Lyu +5
Multi-label class-incremental learning (MLCIL) is essential for real-world multi-label applications, allowing models to learn new labels while retaining previously learned knowledg…
Controllable Continual Test-Time Adaptation
Ziqi Shi, Fan Lyu, Ye Liu +5
Continual Test-Time Adaptation (CTTA) is an emerging and challenging task where a model trained in a source domain must adapt to continuously changing conditions during testing, wi…
Overcoming Domain Drift in Online Continual Learning
Fan Lyu, Daofeng Liu, Linglan Zhao +5
Online Continual Learning (OCL) empowers machine learning models to acquire new knowledge online across a sequence of tasks. However, OCL faces a significant challenge: catastrophi…
Constructing Sample-to-Class Graph for Few-Shot Class-Incremental Learning
Fuyuan Hu, Jian Zhang, Fan Lyu +2
Few-shot class-incremental learning (FSCIL) aims to build machine learning model that can continually learn new concepts from a few data samples, without forgetting knowledge of ol…
Dynamic V2X Autonomous Perception from Road-to-Vehicle Vision
Jiayao Tan, Fan Lyu, Linyan Li +4
Vehicle-to-everything (V2X) perception is an innovative technology that enhances vehicle perception accuracy, thereby elevating the security and reliability of autonomous systems.…