128 citations · 206 across the 16 of their papers we have counts for
7 papers · 1 filter
MobileTL: On-device Transfer Learning with Inverted Residual Blocks
Hung-Yueh Chiang, Natalia Frumkin, Feng Liang +1
Transfer learning on edge is challenging due to on-device limited resources. Existing work addresses this issue by training a subset of parameters or adding model patches. Develope…
Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP
Feng Liang, Bichen Wu, Xiaoliang Dai +6
Open-vocabulary semantic segmentation aims to segment an image into semantic regions according to text descriptions, which may not have been seen during training. Recent two-stage…
Learned Image Compression with Generalized Octave Convolution and Cross-Resolution Parameter Estimation
Haisheng Fu, Feng Liang
The application of the context-adaptive entropy model significantly improves the rate-distortion (R-D) performance, in which hyperpriors and autoregressive models are jointly utili…
Asymmetric Learned Image Compression with Multi-Scale Residual Block, Importance Map, and Post-Quantization Filtering
Haisheng Fu, Feng Liang, Jie Liang +3
Recently, deep learning-based image compression has made signifcant progresses, and has achieved better ratedistortion (R-D) performance than the latest traditional method, H.266/V…
SupMAE: Supervised Masked Autoencoders Are Efficient Vision Learners
Feng Liang, Yangguang Li, Diana Marculescu
Recently, self-supervised Masked Autoencoders (MAE) have attracted unprecedented attention for their impressive representation learning ability. However, the pretext task, Masked I…
Democratizing Contrastive Language-Image Pre-training: A CLIP Benchmark of Data, Model, and Supervision
Yufeng Cui, Lichen Zhao, Feng Liang +2
Contrastive Language-Image Pretraining (CLIP) has emerged as a novel paradigm to learn visual models from language supervision. While researchers continue to push the frontier of C…
RePre: Improving Self-Supervised Vision Transformer with Reconstructive Pre-training
Luya Wang, Feng Liang, Yangguang Li +3
Recently, self-supervised vision transformers have attracted unprecedented attention for their impressive representation learning ability. However, the dominant method, contrastive…