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20212024
most citedLearning a Condensed Frame for Memory-Efficient Video Class-Incremental Learning

12 citations · 18 across the 6 of their papers we have counts for

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6 papers · 1 filter

cs.CV2024

SimMAT: Exploring Transferability from Vision Foundation Models to Any Image Modality

Chenyang Lei, Liyi Chen, Jun Cen +6

Foundation models like ChatGPT and Sora that are trained on a huge scale of data have made a revolutionary social impact. However, it is extremely challenging for sensors in many d…

cs.CV202212 cited

Learning a Condensed Frame for Memory-Efficient Video Class-Incremental Learning

Yixuan Pei, Zhiwu Qing, Jun Cen +6

Recent incremental learning for action recognition usually stores representative videos to mitigate catastrophic forgetting. However, only a few bulky videos can be stored due to t…

cs.CV20221 cited

A lightweight multi-scale context network for salient object detection in optical remote sensing images

Yuhan Lin, Han Sun, Ningzhong Liu +3

Due to the more dramatic multi-scale variations and more complicated foregrounds and backgrounds in optical remote sensing images (RSIs), the salient object detection (SOD) for opt…

cs.CV20212 cited

Deep Metric Learning for Open World Semantic Segmentation

Jun Cen, Peng Yun, Junhao Cai +2

Classical close-set semantic segmentation networks have limited ability to detect out-of-distribution (OOD) objects, which is important for safety-critical applications such as aut…

cs.CV20212 cited

MPI: Multi-receptive and Parallel Integration for Salient Object Detection

Han Sun, Jun Cen, Ningzhong Liu +2

The semantic representation of deep features is essential for image context understanding, and effective fusion of features with different semantic representations can significantl…

cs.CV20211 cited

BORM: Bayesian Object Relation Model for Indoor Scene Recognition

Liguang Zhou, Jun Cen, Xingchao Wang +3

Scene recognition is a fundamental task in robotic perception. For human beings, scene recognition is reasonable because they have abundant object knowledge of the real world. The…