Gait Recognition in the Wild: A Large-scale Benchmark and NAS-based Baseline

Kavli Affiliate: Zheng Zhu | First 5 Authors: Xianda Guo, Zheng Zhu, Tian Yang, Beibei Lin, Junjie Huang | Summary: Gait benchmarks empower the research community to train and evaluate high-performance gait recognition systems. Even though growing efforts have been devoted to cross-view recognition, academia is restricted by current existing databases captured in the controlled […]


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Half-Wormholes and Ensemble Averages

Kavli Affiliate: Cheng Peng | First 5 Authors: Cheng Peng, Jia Tian, Yingyu Yang, , | Summary: We study "half-wormhole-like" saddle point contributions to spectral correlators in a variety of ensemble average models, including various statistical models, generalized 0d SYK models, 1d Brownian SYK models and an extension of it. In statistical ensemble models, where […]


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DDDM: a Brain-Inspired Framework for Robust Classification

Kavli Affiliate: Yi Zhou | First 5 Authors: Xiyuan Chen, Xingyu Li, Yi Zhou, Tianming Yang, | Summary: Despite their outstanding performance in a broad spectrum of real-world tasks, deep artificial neural networks are sensitive to input noises, particularly adversarial perturbations. On the contrary, human and animal brains are much less vulnerable. In contrast to […]


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SHAPE: An Unified Approach to Evaluate the Contribution and Cooperation of Individual Modalities

Kavli Affiliate: Yi Zhou | First 5 Authors: Pengbo Hu, Xingyu Li, Yi Zhou, , | Summary: As deep learning advances, there is an ever-growing demand for models capable of synthesizing information from multi-modal resources to address the complex tasks raised from real-life applications. Recently, many large multi-modal datasets have been collected, on which researchers […]


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Reliable Label Correction is a Good Booster When Learning with Extremely Noisy Labels

Kavli Affiliate: Zheng Zhu | First 5 Authors: Kai Wang, Xiangyu Peng, Shuo Yang, Jianfei Yang, Zheng Zhu | Summary: Learning with noisy labels has aroused much research interest since data annotations, especially for large-scale datasets, may be inevitably imperfect. Recent approaches resort to a semi-supervised learning problem by dividing training samples into clean and […]


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Modeling Ride-Sourcing Matching and Pickup Processes based on Additive Gaussian Process Models

Kavli Affiliate: Zheng Zhu | First 5 Authors: Zheng Zhu, Meng Xu, Yining Di, Xiqun Chen, Jingru Yu | Summary: Matching and pickup processes are core features of ride-sourcing services. Previous studies have adopted abundant analytical models to depict the two processes and obtain operational insights; while the goodness of fit between models and data […]


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Model predictive control of agro-hydrological systems based on a two-layer neural network modeling framework

Kavli Affiliate: Biao Huang | First 5 Authors: Zhiyinan Huang, Jinfeng Liu, Biao Huang, , | Summary: Water scarcity is an urgent issue to be resolved and improving irrigation water-use efficiency through closed-loop control is essential. The complex agro-hydrological system dynamics, however, often pose challenges in closed-loop control applications. In this work, we propose a […]


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Share With Thy Neighbors: Single-View Reconstruction by Cross-Instance Consistency

Kavli Affiliate: Matthew Fisher | First 5 Authors: Tom Monnier, Matthew Fisher, Alexei A. Efros, Mathieu Aubry, | Summary: Approaches to single-view reconstruction typically rely on viewpoint annotations, silhouettes, the absence of background, multiple views of the same instance, a template shape, or symmetry. We avoid all of these supervisions and hypotheses by leveraging explicitly […]


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Share With Thy Neighbors: Single-View Reconstruction by Cross-Instance Consistency

Kavli Affiliate: Matthew Fisher | First 5 Authors: Tom Monnier, Matthew Fisher, Alexei A. Efros, Mathieu Aubry, | Summary: Approaches to single-view reconstruction typically rely on viewpoint annotations, silhouettes, the absence of background, multiple views of the same instance, a template shape, or symmetry. We avoid all of these supervisions and hypotheses by leveraging explicitly […]


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WebFace260M: A Benchmark for Million-Scale Deep Face Recognition

Kavli Affiliate: Zheng Zhu | First 5 Authors: Zheng Zhu, Guan Huang, Jiankang Deng, Yun Ye, Junjie Huang | Summary: Face benchmarks empower the research community to train and evaluate high-performance face recognition systems. In this paper, we contribute a new million-scale recognition benchmark, containing uncurated 4M identities/260M faces (WebFace260M) and cleaned 2M identities/42M faces […]


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