19 citations · 45 across the 5 of their papers we have counts for
6 papers
SST: Self-training with Self-adaptive Thresholding for Semi-supervised Learning
Shuai Zhao, Heyan Huang, Xinge Li +2
Neural networks have demonstrated exceptional performance in supervised learning, benefiting from abundant high-quality annotated data. However, obtaining such data in real-world s…
Understanding Self-Supervised Pretraining with Part-Aware Representation Learning
Jie Zhu, Jiyang Qi, Mingyu Ding +6
In this paper, we are interested in understanding self-supervised pretraining through studying the capability that self-supervised representation pretraining methods learn part-awa…
CAE v2: Context Autoencoder with CLIP Target
Xinyu Zhang, Jiahui Chen, Junkun Yuan +10
Masked image modeling (MIM) learns visual representation by masking and reconstructing image patches. Applying the reconstruction supervision on the CLIP representation has been pr…
Group DETR v2: Strong Object Detector with Encoder-Decoder Pretraining
Qiang Chen, Jian Wang, Chuchu Han +12
We present a strong object detector with encoder-decoder pretraining and finetuning. Our method, called Group DETR v2, is built upon a vision transformer encoder ViT-Huge~\cite{dos…
Context Autoencoder for Self-Supervised Representation Learning
Xiaokang Chen, Mingyu Ding, Xiaodi Wang +7
We present a novel masked image modeling (MIM) approach, context autoencoder (CAE), for self-supervised representation pretraining. We pretrain an encoder by making predictions in…
Conditional DETR for Fast Training Convergence
Depu Meng, Xiaokang Chen, Zejia Fan +5
The recently-developed DETR approach applies the transformer encoder and decoder architecture to object detection and achieves promising performance. In this paper, we handle the c…