68 citations · 110 across the 7 of their papers we have counts for
7 papers
ALIP: Adaptive Language-Image Pre-training with Synthetic Caption
Kaicheng Yang, Jiankang Deng, Xiang An +5
Contrastive Language-Image Pre-training (CLIP) has significantly boosted the performance of various vision-language tasks by scaling up the dataset with image-text pairs collected…
Unicom: Universal and Compact Representation Learning for Image Retrieval
Xiang An, Jiankang Deng, Kaicheng Yang +5
Modern image retrieval methods typically rely on fine-tuning pre-trained encoders to extract image-level descriptors. However, the most widely used models are pre-trained on ImageN…
Killing Two Birds with One Stone:Efficient and Robust Training of Face Recognition CNNs by Partial FC
Xiang An, Jiankang Deng, Jia Guo +4
Learning discriminative deep feature embeddings by using million-scale in-the-wild datasets and margin-based softmax loss is the current state-of-the-art approach for face recognit…
Pre-training strategies and datasets for facial representation learning
Adrian Bulat, Shiyang Cheng, Jing Yang +3
What is the best way to learn a universal face representation? Recent work on Deep Learning in the area of face analysis has focused on supervised learning for specific tasks of in…
Training Binary Neural Networks with Real-to-Binary Convolutions
Brais Martinez, Jing Yang, Adrian Bulat +1
This paper shows how to train binary networks to within a few percent points () of the full precision counterpart. We first show how to build a strong baseline, which…
Knowledge distillation via adaptive instance normalization
Jing Yang, Brais Martinez, Adrian Bulat +1
This paper addresses the problem of model compression via knowledge distillation. To this end, we propose a new knowledge distillation method based on transferring feature statisti…