64 citations · 198 across the 12 of their papers we have counts for
12 papers
From Noise to Order: Learning to Rank via Denoising Diffusion
Sajad Ebrahimi, Bhaskar Mitra, Negar Arabzadeh +4
In information retrieval (IR), learning-to-rank (LTR) methods have traditionally limited themselves to discriminative machine learning approaches that model the probability of the…
AGG: Amortized Generative 3D Gaussians for Single Image to 3D
Dejia Xu, Ye Yuan, Morteza Mardani +4
Given the growing need for automatic 3D content creation pipelines, various 3D representations have been studied to generate 3D objects from a single image. Due to its superior ren…
Font Completion and Manipulation by Cycling Between Multi-Modality Representations
Ye Yuan, Wuyang Chen, Zhaowen Wang +4
Generating font glyphs of consistent style from one or a few reference glyphs, i.e., font completion, is an important task in topographical design. As the problem is more well-defi…
Black-Box Diagnosis and Calibration on GAN Intra-Mode Collapse: A Pilot Study
Zhenyu Wu, Zhaowen Wang, Ye Yuan +3
Generative adversarial networks (GANs) nowadays are capable of producing images of incredible realism. One concern raised is whether the state-of-the-art GAN's learned distribution…
PCAL: A Privacy-preserving Intelligent Credit Risk Modeling Framework Based on Adversarial Learning
Yuli Zheng, Zhenyu Wu, Ye Yuan +2
Credit risk modeling has permeated our everyday life. Most banks and financial companies use this technique to model their clients' trustworthiness. While machine learning is incre…
AutoPose: Searching Multi-Scale Branch Aggregation for Pose Estimation
Xinyu Gong, Wuyang Chen, Yifan Jiang +5
We present AutoPose, a novel neural architecture search(NAS) framework that is capable of automatically discovering multiple parallel branches of cross-scale connections towards ac…
Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training
Xuxi Chen, Wuyang Chen, Tianlong Chen +4
Many real-world applications have to tackle the Positive-Unlabeled (PU) learning problem, i.e., learning binary classifiers from a large amount of unlabeled data and a few labeled…
Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery
Zepeng Huo, Arash PakBin, Xiaohan Chen +6
Activity recognition in wearable computing faces two key challenges: i) activity characteristics may be context-dependent and change under different contexts or situations; ii) unk…
In Defense of the Triplet Loss Again: Learning Robust Person Re-Identification with Fast Approximated Triplet Loss and Label Distillation
Ye Yuan, Wuyang Chen, Yang Yang +1
The comparative losses (typically, triplet loss) are appealing choices for learning person re-identification (ReID) features. However, the triplet loss is computationally much more…
Calibrated Domain-Invariant Learning for Highly Generalizable Large Scale Re-Identification
Ye Yuan, Wuyang Chen, Tianlong Chen +4
Many real-world applications, such as city-scale traffic monitoring and control, requires large-scale re-identification. However, previous ReID methods often failed to address two…
ABD-Net: Attentive but Diverse Person Re-Identification
Tianlong Chen, Shaojin Ding, Jingyi Xie +5
Attention mechanism has been shown to be effective for person re-identification (Re-ID). However, the learned attentive feature embeddings which are often not naturally diverse nor…
Bridging the Gap Between Computational Photography and Visual Recognition
Rosaura G. VidalMata, Sreya Banerjee, Brandon RichardWebster +21
What is the current state-of-the-art for image restoration and enhancement applied to degraded images acquired under less than ideal circumstances? Can the application of such algo…