312 citations · 1k across the 25 of their papers we have counts for
7 papers · 1 filter
Masked Autoencoders are Efficient Class Incremental Learners
Jiang-Tian Zhai, Xialei Liu, Andrew D. Bagdanov +2
Class Incremental Learning (CIL) aims to sequentially learn new classes while avoiding catastrophic forgetting of previous knowledge. We propose to use Masked Autoencoders (MAEs) a…
Masked Cross-image Encoding for Few-shot Segmentation
Wenbo Xu, Huaxi Huang, Ming Cheng +3
Few-shot segmentation (FSS) is a dense prediction task that aims to infer the pixel-wise labels of unseen classes using only a limited number of annotated images. The key challenge…
YOLO-MS: Rethinking Multi-Scale Representation Learning for Real-time Object Detection
Yuming Chen, Xinbin Yuan, Jiabao Wang +4
We aim at providing the object detection community with an efficient and performant object detector, termed YOLO-MS. The core design is based on a series of investigations on how m…
Delving Deeper into Data Scaling in Masked Image Modeling
Cheng-Ze Lu, Xiaojie Jin, Qibin Hou +3
Understanding whether self-supervised learning methods can scale with unlimited data is crucial for training large-scale models. In this work, we conduct an empirical study on the…
Co-Salient Object Detection with Co-Representation Purification
Ziyue Zhu, Zhao Zhang, Zheng Lin +2
Co-salient object detection (Co-SOD) aims at discovering the common objects in a group of relevant images. Mining a co-representation is essential for locating co-salient objects.…
MDTv2: Masked Diffusion Transformer is a Strong Image Synthesizer
Shanghua Gao, Pan Zhou, Ming-Ming Cheng +1
Despite its success in image synthesis, we observe that diffusion probabilistic models (DPMs) often lack contextual reasoning ability to learn the relations among object parts in a…
CMAE-V: Contrastive Masked Autoencoders for Video Action Recognition
Cheng-Ze Lu, Xiaojie Jin, Zhicheng Huang +3
Contrastive Masked Autoencoder (CMAE), as a new self-supervised framework, has shown its potential of learning expressive feature representations in visual image recognition. This…