16 citations · 80 across the 15 of their papers we have counts for
15 papers
GMConv: Modulating Effective Receptive Fields for Convolutional Kernels
Qi Chen, Chao Li, Jia Ning +2
In convolutional neural networks, the convolutions are conventionally performed using a square kernel with a fixed N N receptive field (RF). However, what matters most to…
Randomized Quantization: A Generic Augmentation for Data Agnostic Self-supervised Learning
Huimin Wu, Chenyang Lei, Xiao Sun +5
Self-supervised representation learning follows a paradigm of withholding some part of the data and tasking the network to predict it from the remaining part. Among many techniques…
ClipCrop: Conditioned Cropping Driven by Vision-Language Model
Zhihang Zhong, Mingxi Cheng, Zhirong Wu +7
Image cropping has progressed tremendously under the data-driven paradigm. However, current approaches do not account for the intentions of the user, which is an issue especially w…
Local Magnification for Data and Feature Augmentation
Kun He, Chang Liu, Stephen Lin +1
In recent years, many data augmentation techniques have been proposed to increase the diversity of input data and reduce the risk of overfitting on deep neural networks. In this wo…
Could Giant Pretrained Image Models Extract Universal Representations?
Yutong Lin, Ze Liu, Zheng Zhang +4
Frozen pretrained models have become a viable alternative to the pretraining-then-finetuning paradigm for transfer learning. However, with frozen models there are relatively few pa…
Integrative Feature and Cost Aggregation with Transformers for Dense Correspondence
Sunghwan Hong, Seokju Cho, Seungryong Kim +1
We present a novel architecture for dense correspondence. The current state-of-the-art are Transformer-based approaches that focus on either feature descriptors or cost volume aggr…
Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation
Sunghwan Hong, Seokju Cho, Jisu Nam +2
This paper presents a novel cost aggregation network, called Volumetric Aggregation with Transformers (VAT), for few-shot segmentation. The use of transformers can benefit correlat…
Animation from Blur: Multi-modal Blur Decomposition with Motion Guidance
Zhihang Zhong, Xiao Sun, Zhirong Wu +3
We study the challenging problem of recovering detailed motion from a single motion-blurred image. Existing solutions to this problem estimate a single image sequence without consi…
Extreme Masking for Learning Instance and Distributed Visual Representations
Zhirong Wu, Zihang Lai, Xiao Sun +1
The paper presents a scalable approach for learning spatially distributed visual representations over individual tokens and a holistic instance representation simultaneously. We us…
Region Rebalance for Long-Tailed Semantic Segmentation
Jiequan Cui, Yuhui Yuan, Zhisheng Zhong +4
In this paper, we study the problem of class imbalance in semantic segmentation. We first investigate and identify the main challenges of addressing this issue through pixel rebala…
Bringing Rolling Shutter Images Alive with Dual Reversed Distortion
Zhihang Zhong, Mingdeng Cao, Xiao Sun +5
Rolling shutter (RS) distortion can be interpreted as the result of picking a row of pixels from instant global shutter (GS) frames over time during the exposure of the RS camera.…
A Simple Multi-Modality Transfer Learning Baseline for Sign Language Translation
Yutong Chen, Fangyun Wei, Xiao Sun +2
This paper proposes a simple transfer learning baseline for sign language translation. Existing sign language datasets (e.g. PHOENIX-2014T, CSL-Daily) contain only about 10K-20K pa…
Cross-Model Pseudo-Labeling for Semi-Supervised Action Recognition
Yinghao Xu, Fangyun Wei, Xiao Sun +5
Semi-supervised action recognition is a challenging but important task due to the high cost of data annotation. A common approach to this problem is to assign unlabeled data with p…
Explainable AI in Credit Risk Management
Branka Hadji Misheva, Joerg Osterrieder, Ali Hirsa +2
Artificial Intelligence (AI) has created the single biggest technology revolution the world has ever seen. For the finance sector, it provides great opportunities to enhance custom…
Cross-Iteration Batch Normalization
Zhuliang Yao, Yue Cao, Shuxin Zheng +2
A well-known issue of Batch Normalization is its significantly reduced effectiveness in the case of small mini-batch sizes. When a mini-batch contains few examples, the statistics…