222 citations · 261 across the 10 of their papers we have counts for
9 papers · 1 filter
DMT: Comprehensive Distillation with Multiple Self-supervised Teachers
Yuang Liu, Jing Wang, Qiang Zhou +3
Numerous self-supervised learning paradigms, such as contrastive learning and masked image modeling, have been proposed to acquire powerful and general representations from unlabel…
Language-guided Few-shot Semantic Segmentation
Jing Wang, Yuang Liu, Qiang Zhou +1
Few-shot learning is a promising way for reducing the label cost in new categories adaptation with the guidance of a small, well labeled support set. But for few-shot semantic segm…
ES-MVSNet: Efficient Framework for End-to-end Self-supervised Multi-View Stereo
Qiang Zhou, Chaohui Yu, Jingliang Li +3
Compared to the multi-stage self-supervised multi-view stereo (MVS) method, the end-to-end (E2E) approach has received more attention due to its concise and efficient training pipe…
Dynamic Token-Pass Transformers for Semantic Segmentation
Yuang Liu, Qiang Zhou, Jing Wang +3
Vision transformers (ViT) usually extract features via forwarding all the tokens in the self-attention layers from top to toe. In this paper, we introduce dynamic token-pass vision…
LMSeg: Language-guided Multi-dataset Segmentation
Qiang Zhou, Yuang Liu, Chaohui Yu +3
It's a meaningful and attractive topic to build a general and inclusive segmentation model that can recognize more categories in various scenarios. A straightforward way is to comb…
Source-Free Domain Adaptation for Semantic Segmentation
Yuang Liu, Wei Zhang, Jun Wang
Unsupervised Domain Adaptation (UDA) can tackle the challenge that convolutional neural network(CNN)-based approaches for semantic segmentation heavily rely on the pixel-level anno…
Zero-shot Adversarial Quantization
Yuang Liu, Wei Zhang, Jun Wang
Model quantization is a promising approach to compress deep neural networks and accelerate inference, making it possible to be deployed on mobile and edge devices. To retain the hi…
Adaptive Multi-Teacher Multi-level Knowledge Distillation
Yuang Liu, Wei Zhang, Jun Wang
Knowledge distillation~(KD) is an effective learning paradigm for improving the performance of lightweight student networks by utilizing additional supervision knowledge distilled…
Learning from a Lightweight Teacher for Efficient Knowledge Distillation
Yuang Liu, Wei Zhang, Jun Wang
Knowledge Distillation (KD) is an effective framework for compressing deep learning models, realized by a student-teacher paradigm requiring small student networks to mimic the sof…