30 citations · 66 across the 16 of their papers we have counts for
16 papers
Super-Eddington Accretion as a Possible Scenario to Form GW190425
W. T. Zhang, Z. H. T. Wang, J. -P. Zhu +7
On 2019 April 25, the LIGO/Virgo Scientific Collaboration detected a compact binary coalescence, GW190425. Under the assumption of the binary neutron star (BNS), the total mass of…
Revisiting the Properties of GW190814 and Its Formation History
F. Lyu, L. Yuan, D. H. Wu +9
GW190814 was reported during LIGO's and Virgo's third observing run with the most asymmetric component masses (a black hole and a compac…
Two-level Graph Network for Few-Shot Class-Incremental Learning
Hao Chen, Linyan Li, Fan Lyu +3
Few-shot class-incremental learning (FSCIL) aims to design machine learning algorithms that can continually learn new concepts from a few data points, without forgetting knowledge…
Centroid Distance Distillation for Effective Rehearsal in Continual Learning
Daofeng Liu, Fan Lyu, Linyan Li +2
Rehearsal, retraining on a stored small data subset of old tasks, has been proven effective in solving catastrophic forgetting in continual learning. However, due to the sampled da…
Multi-Label Continual Learning using Augmented Graph Convolutional Network
Kaile Du, Fan Lyu, Linyan Li +5
Multi-Label Continual Learning (MLCL) builds a class-incremental framework in a sequential multi-label image recognition data stream. The critical challenges of MLCL are the constr…
Exploring Example Influence in Continual Learning
Qing Sun, Fan Lyu, Fanhua Shang +2
Continual Learning (CL) sequentially learns new tasks like human beings, with the goal to achieve better Stability (S, remembering past tasks) and Plasticity (P, adapting to new ta…
Class-Incremental Lifelong Learning in Multi-Label Classification
Kaile Du, Linyan Li, Fan Lyu +3
Existing class-incremental lifelong learning studies only the data is with single-label, which limits its adaptation to multi-label data. This paper studies Lifelong Multi-Label (L…
AGCN: Augmented Graph Convolutional Network for Lifelong Multi-label Image Recognition
Kaile Du, Fan Lyu, Fuyuan Hu +4
The Lifelong Multi-Label (LML) image recognition builds an online class-incremental classifier in a sequential multi-label image recognition data stream. The key challenges of LML…
Each Attribute Matters: Contrastive Attention for Sentence-based Image Editing
Liuqing Zhao, Fan Lyu, Fuyuan Hu +3
Sentence-based Image Editing (SIE) aims to deploy natural language to edit an image. Offering potentials to reduce expensive manual editing, SIE has attracted much interest recentl…
Disentangling Semantic-to-visual Confusion for Zero-shot Learning
Zihan Ye, Fuyuan Hu, Fan Lyu +2
Using generative models to synthesize visual features from semantic distribution is one of the most popular solutions to ZSL image classification in recent years. The triplet loss…
Coarse to Fine: Multi-label Image Classification with Global/Local Attention
Fan Lyu, Fuyuan Hu, Victor S. Sheng +3
In our daily life, the scenes around us are always with multiple labels especially in a smart city, i.e., recognizing the information of city operation to response and control. Gre…
Multi-Domain Multi-Task Rehearsal for Lifelong Learning
Fan Lyu, Shuai Wang, Wei Feng +3
Rehearsal, seeking to remind the model by storing old knowledge in lifelong learning, is one of the most effective ways to mitigate catastrophic forgetting, i.e., biased forgetting…
Associating Multi-Scale Receptive Fields for Fine-grained Recognition
Zihan Ye, Fuyuan Hu, Yin Liu +3
Extracting and fusing part features have become the key of fined-grained image recognition. Recently, Non-local (NL) module has shown excellent improvement in image recognition. Ho…
Modeling Cross-view Interaction Consistency for Paired Egocentric Interaction Recognition
Zhongguo Li, Fan Lyu, Wei Feng +1
With the development of Augmented Reality (AR), egocentric action recognition (EAR) plays important role in accurately understanding demands from the user. However, EAR is designed…
MUTATT: Visual-Textual Mutual Guidance for Referring Expression Comprehension
Shuai Wang, Fan Lyu, Wei Feng +1
Referring expression comprehension (REC) aims to localize a text-related region in a given image by a referring expression in natural language. Existing methods focus on how to bui…
SR-GAN: Semantic Rectifying Generative Adversarial Network for Zero-shot Learning
Zihan Ye, Fan Lyu, Linyan Li +3
The existing Zero-Shot learning (ZSL) methods may suffer from the vague class attributes that are highly overlapped for different classes. Unlike these methods that ignore the disc…