128 citations · 932 across the 39 of their papers we have counts for
6 papers · 1 filter
PalGAN: Image Colorization with Palette Generative Adversarial Networks
Yi Wang, Menghan Xia, Lu Qi +2
Multimodal ambiguity and color bleeding remain challenging in colorization. To tackle these problems, we propose a new GAN-based colorization approach PalGAN, integrated with palet…
ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning
Junting Pan, Ziyi Lin, Xiatian Zhu +2
Capitalizing on large pre-trained models for various downstream tasks of interest have recently emerged with promising performance. Due to the ever-growing model size, the standard…
X-Learner: Learning Cross Sources and Tasks for Universal Visual Representation
Yinan He, Gengshi Huang, Siyu Chen +7
In computer vision, pre-training models based on largescale supervised learning have been proven effective over the past few years. However, existing works mostly focus on learning…
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…
Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine Synergy
Yuanhan Zhang, Qinghong Sun, Yichun Zhou +7
Large-scale datasets play a vital role in computer vision. But current datasets are annotated blindly without differentiation to samples, making the data collection inefficient and…
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…