no code implementations • CVPR 2023 • Xuanyi Du, Weitao Wan, Chong Sun, Chen Li
We propose a novel Knowledge Transfer (KT) loss which simultaneously distills the knowledge of objectness and class entropy from a proposal generator trained on the S dataset to optimize a multiple instance learning module on the T dataset.
no code implementations • 27 Nov 2022 • Luca Thiede, Chong Sun, Alán Aspuru-Guzik
In this paper, we introduce four main novelties: First, we present a new way of handling the topology problem of normalizing flows.
1 code implementation • 31 Mar 2022 • Mario Krenn, Qianxiang Ai, Senja Barthel, Nessa Carson, Angelo Frei, Nathan C. Frey, Pascal Friederich, Théophile Gaudin, Alberto Alexander Gayle, Kevin Maik Jablonka, Rafael F. Lameiro, Dominik Lemm, Alston Lo, Seyed Mohamad Moosavi, José Manuel Nápoles-Duarte, AkshatKumar Nigam, Robert Pollice, Kohulan Rajan, Ulrich Schatzschneider, Philippe Schwaller, Marta Skreta, Berend Smit, Felix Strieth-Kalthoff, Chong Sun, Gary Tom, Guido Falk von Rudorff, Andrew Wang, Andrew White, Adamo Young, Rose Yu, Alán Aspuru-Guzik
We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science.
no code implementations • 27 Mar 2022 • Xingxuan Zhang, Zekai Xu, Renzhe Xu, Jiashuo Liu, Peng Cui, Weitao Wan, Chong Sun, Chen Li
Despite the striking performance achieved by modern detectors when training and test data are sampled from the same or similar distribution, the generalization ability of detectors under unknown distribution shifts remains hardly studied.
no code implementations • CVPR 2022 • Junyi Pan, Chong Sun, Yizhou Zhou, Ying Zhang, Chen Li
We first theoretically investigate how the weight coupling problem affects the network searching performance from a parameter distribution perspective, and then propose a novel supernet training strategy with a Distribution Consistent Constraint that can provide a good measurement for the extent to which two architectures can share weights.
no code implementations • 5 Jan 2020 • Lizhao Gao, Hai-Hua Xu, Chong Sun, Junling Liu, Yu-Wing Tai
Existing approaches for fine-grained visual recognition focus on learning marginal region-based representations while neglecting the spatial and scale misalignments, leading to inferior performance.
1 code implementation • CVPR 2019 • Yuxuan Sun, Chong Sun, Dong Wang, You He, Huchuan Lu
The ROI (region-of-interest) based pooling method performs pooling operations on the cropped ROI regions for various samples and has shown great success in the object detection methods.
1 code implementation • 23 Jan 2019 • Mario Motta, Chong Sun, Adrian Teck Keng Tan, Matthew J. O' Rourke, Erika Ye, Austin J. Minnich, Fernando G. S. L. Brandao, Garnet Kin-Lic Chan
An efficient way to compute Hamiltonian ground-states on a quantum computer stands to impact many problems in the physical and computer sciences, ranging from quantum simulation to machine learning.
Quantum Physics
1 code implementation • CVPR 2018 • Chong Sun, Dong Wang, Huchuan Lu, Ming-Hsuan Yang
To address this issue, we propose a novel CF-based optimization problem to jointly model the discrimination and reliability information.
1 code implementation • CVPR 2018 • Chong Sun, Dong Wang, Huchuan Lu, Ming-Hsuan Yang
Second, we propose a fully convolutional neural network with spatially regularized kernels, through which the filter kernel corresponding to each output channel is forced to focus on a specific region of the target.
Ranked #11 on Visual Object Tracking on VOT2017/18