13 citations · 76 across the 20 of their papers we have counts for
20 papers
HResFormer: Hybrid Residual Transformer for Volumetric Medical Image Segmentation
Sucheng Ren, Xiaomeng Li
Vision Transformer shows great superiority in medical image segmentation due to the ability in learning long-range dependency. For medical image segmentation from 3D data, such as…
SG-Former: Self-guided Transformer with Evolving Token Reallocation
Sucheng Ren, Xingyi Yang, Songhua Liu +1
Vision Transformer has demonstrated impressive success across various vision tasks. However, its heavy computation cost, which grows quadratically with respect to the token sequenc…
NPF-200: A Multi-Modal Eye Fixation Dataset and Method for Non-Photorealistic Videos
Ziyu Yang, Sucheng Ren, Zongwei Wu +4
Non-photorealistic videos are in demand with the wave of the metaverse, but lack of sufficient research studies. This work aims to take a step forward to understand how humans perc…
DeepMIM: Deep Supervision for Masked Image Modeling
Sucheng Ren, Fangyun Wei, Samuel Albanie +2
Deep supervision, which involves extra supervisions to the intermediate features of a neural network, was widely used in image classification in the early deep learning era since i…
TinyMIM: An Empirical Study of Distilling MIM Pre-trained Models
Sucheng Ren, Fangyun Wei, Zheng Zhang +1
Masked image modeling (MIM) performs strongly in pre-training large vision Transformers (ViTs). However, small models that are critical for real-world applications cannot or only m…
Learning from Multiple Annotator Noisy Labels via Sample-wise Label Fusion
Zhengqi Gao, Fan-Keng Sun, Mingran Yang +7
Data lies at the core of modern deep learning. The impressive performance of supervised learning is built upon a base of massive accurately labeled data. However, in some real-worl…
DynaST: Dynamic Sparse Transformer for Exemplar-Guided Image Generation
Songhua Liu, Jingwen Ye, Sucheng Ren +1
One key challenge of exemplar-guided image generation lies in establishing fine-grained correspondences between input and guided images. Prior approaches, despite the promising res…
A Simple Data Mixing Prior for Improving Self-Supervised Learning
Sucheng Ren, Huiyu Wang, Zhengqi Gao +4
Data mixing (e.g., Mixup, Cutmix, ResizeMix) is an essential component for advancing recognition models. In this paper, we focus on studying its effectiveness in the self-supervise…
The Modality Focusing Hypothesis: Towards Understanding Crossmodal Knowledge Distillation
Zihui Xue, Zhengqi Gao, Sucheng Ren +1
Crossmodal knowledge distillation (KD) extends traditional knowledge distillation to the area of multimodal learning and demonstrates great success in various applications. To achi…
Glance to Count: Learning to Rank with Anchors for Weakly-supervised Crowd Counting
Zheng Xiong, Liangyu Chai, Wenxi Liu +3
Crowd image is arguably one of the most laborious data to annotate. In this paper, we devote to reduce the massive demand of densely labeled crowd data, and propose a novel weakly-…
Training-Free Robust Multimodal Learning via Sample-Wise Jacobian Regularization
Zhengqi Gao, Sucheng Ren, Zihui Xue +2
Multimodal fusion emerges as an appealing technique to improve model performances on many tasks. Nevertheless, the robustness of such fusion methods is rarely involved in the prese…
Self-supervision through Random Segments with Autoregressive Coding (RandSAC)
Tianyu Hua, Yonglong Tian, Sucheng Ren +3
Inspired by the success of self-supervised autoregressive representation learning in natural language (GPT and its variants), and advances in recent visual architecture design with…
Shunted Self-Attention via Multi-Scale Token Aggregation
Sucheng Ren, Daquan Zhou, Shengfeng He +2
Recent Vision Transformer~(ViT) models have demonstrated encouraging results across various computer vision tasks, thanks to their competence in modeling long-range dependencies of…
Reducing Spatial Labeling Redundancy for Semi-supervised Crowd Counting
Yongtuo Liu, Sucheng Ren, Liangyu Chai +4
Labeling is onerous for crowd counting as it should annotate each individual in crowd images. Recently, several methods have been proposed for semi-supervised crowd counting to red…
Fine-grained Domain Adaptive Crowd Counting via Point-derived Segmentation
Yongtuo Liu, Dan Xu, Sucheng Ren +3
Due to domain shift, a large performance drop is usually observed when a trained crowd counting model is deployed in the wild. While existing domain-adaptive crowd counting methods…
Unifying Global-Local Representations in Salient Object Detection with Transformer
Sucheng Ren, Qiang Wen, Nanxuan Zhao +2
The fully convolutional network (FCN) has dominated salient object detection for a long period. However, the locality of CNN requires the model deep enough to have a global recepti…
Co-advise: Cross Inductive Bias Distillation
Sucheng Ren, Zhengqi Gao, Tianyu Hua +4
Transformers recently are adapted from the community of natural language processing as a promising substitute of convolution-based neural networks for visual learning tasks. Howeve…
On Feature Decorrelation in Self-Supervised Learning
Tianyu Hua, Wenxiao Wang, Zihui Xue +3
In self-supervised representation learning, a common idea behind most of the state-of-the-art approaches is to enforce the robustness of the representations to predefined augmentat…
Multimodal Knowledge Expansion
Zihui Xue, Sucheng Ren, Zhengqi Gao +1
The popularity of multimodal sensors and the accessibility of the Internet have brought us a massive amount of unlabeled multimodal data. Since existing datasets and well-trained m…
TENet: Triple Excitation Network for Video Salient Object Detection
Sucheng Ren, Chu Han, Xin Yang +2
In this paper, we propose a simple yet effective approach, named Triple Excitation Network, to reinforce the training of video salient object detection (VSOD) from three aspects, s…