233 citations · 450 across the 17 of their papers we have counts for
18 papers
L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learning
Yasar Abbas Ur Rehman, Yan Gao, Pedro Porto Buarque de Gusmão +3
The ubiquity of camera-enabled devices has led to large amounts of unlabeled image data being produced at the edge. The integration of self-supervised learning (SSL) and federated…
Source-Aware Embedding Training on Heterogeneous Information Networks
Tsai Hor Chan, Chi Ho Wong, Jiajun Shen +1
Heterogeneous information networks (HINs) have been extensively applied to real-world tasks, such as recommendation systems, social networks, and citation networks. While existing…
Making the Most of What You Have: Adapting Pre-trained Visual Language Models in the Low-data Regime
Chuhan Zhang, Antoine Miech, Jiajun Shen +2
Large-scale visual language models are widely used as pre-trained models and then adapted for various downstream tasks. While humans are known to efficiently learn new tasks from a…
NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research
Jorg Bornschein, Alexandre Galashov, Ross Hemsley +17
A shared goal of several machine learning communities like continual learning, meta-learning and transfer learning, is to design algorithms and models that efficiently and robustly…
SL3D: Self-supervised-Self-labeled 3D Recognition
Fernando Julio Cendra, Lan Ma, Jiajun Shen +1
Deep learning has attained remarkable success in many 3D visual recognition tasks, including shape classification, object detection, and semantic segmentation. However, many of the…
Towards Efficient and Scale-Robust Ultra-High-Definition Image Demoireing
Xin Yu, Peng Dai, Wenbo Li +4
With the rapid development of mobile devices, modern widely-used mobile phones typically allow users to capture 4K resolution (i.e., ultra-high-definition) images. However, for ima…
Federated Self-supervised Learning for Video Understanding
Yasar Abbas Ur Rehman, Yan Gao, Jiajun Shen +2
The ubiquity of camera-enabled mobile devices has lead to large amounts of unlabelled video data being produced at the edge. Although various self-supervised learning (SSL) methods…
Reconstruct Face from Features Using GAN Generator as a Distribution Constraint
Xingbo Dong, Zhihui Miao, Lan Ma +4
Face recognition based on the deep convolutional neural networks (CNN) shows superior accuracy performance attributed to the high discriminative features extracted. Yet, the securi…
Video Demoireing with Relation-Based Temporal Consistency
Peng Dai, Xin Yu, Lan Ma +5
Moire patterns, appearing as color distortions, severely degrade image and video qualities when filming a screen with digital cameras. Considering the increasing demands for captur…
Abandoning the Bayer-Filter to See in the Dark
Xingbo Dong, Wanyan Xu, Zhihui Miao +6
Low-light image enhancement - a pervasive but challenging problem, plays a central role in enhancing the visibility of an image captured in a poor illumination environment. Due to…
Automatic Meta-Path Discovery for Effective Graph-Based Recommendation
Wentao Ning, Reynold Cheng, Jiajun Shen +7
Heterogeneous Information Networks (HINs) are labeled graphs that depict relationships among different types of entities (e.g., users, movies and directors). For HINs, meta-path-ba…
Interpretable performance analysis towards offline reinforcement learning: A dataset perspective
Chenyang Xi, Bo Tang, Jiajun Shen +3
Offline reinforcement learning (RL) has increasingly become the focus of the artificial intelligent research due to its wide real-world applications where the collection of data ma…
Scalable Graph Neural Networks for Heterogeneous Graphs
Lingfan Yu, Jiajun Shen, Jinyang Li +1
Graph neural networks (GNNs) are a popular class of parametric model for learning over graph-structured data. Recent work has argued that GNNs primarily use the graph for feature s…
Facebook AI's WAT19 Myanmar-English Translation Task Submission
Peng-Jen Chen, Jiajun Shen, Matt Le +5
This paper describes Facebook AI's submission to the WAT 2019 Myanmar-English translation task. Our baseline systems are BPE-based transformer models. We explore methods to leverag…
Revisiting Self-Training for Neural Sequence Generation
Junxian He, Jiatao Gu, Jiajun Shen +1
Self-training is one of the earliest and simplest semi-supervised methods. The key idea is to augment the original labeled dataset with unlabeled data paired with the model's predi…
The Source-Target Domain Mismatch Problem in Machine Translation
Jiajun Shen, Peng-Jen Chen, Matt Le +5
While we live in an increasingly interconnected world, different places still exhibit strikingly different cultures and many events we experience in our every day life pertain only…
PyTorch-BigGraph: A Large-scale Graph Embedding System
Adam Lerer, Ledell Wu, Jiajun Shen +4
Graph embedding methods produce unsupervised node features from graphs that can then be used for a variety of machine learning tasks. Modern graphs, particularly in industrial appl…
Deformable Classifiers
Jiajun Shen, Yali Amit
Geometric variations of objects, which do not modify the object class, pose a major challenge for object recognition. These variations could be rigid as well as non-rigid transform…