1 code implementation • 8 Oct 2023 • Yuting Wang, Jinpeng Wang, Bin Chen, Ziyun Zeng, Shu-Tao Xia
Current PRVR methods adopt scanning-based clip construction to achieve explicit clip modeling, which is information-redundant and requires a large storage overhead.
1 code implementation • 22 Sep 2023 • Xinyu Zhang, Yuting Wang, Abdeslam Boularias
DE-ViT establishes new state-of-the-art results on all benchmarks.
Ranked #1 on Few-Shot Object Detection on MS-COCO (30-shot)
1 code implementation • ICCV 2023 • Yuting Wang, Velibor Ilic, Jiatong Li, Branislav Kisacanin, Vladimir Pavlovic
In this work, we propose ALWOD, a new framework that addresses this problem by fusing active learning (AL) with weakly and semi-supervised object detection paradigms.
1 code implementation • 22 Aug 2023 • Jinpeng Wang, Ziyun Zeng, Yunxiao Wang, Yuting Wang, Xingyu Lu, Tianxiang Li, Jun Yuan, Rui Zhang, Hai-Tao Zheng, Shu-Tao Xia
We propose MISSRec, a multi-modal pre-training and transfer learning framework for SR. On the user side, we design a Transformer-based encoder-decoder model, where the contextual encoder learns to capture the sequence-level multi-modal user interests while a novel interest-aware decoder is developed to grasp item-modality-interest relations for better sequence representation.
1 code implementation • NeurIPS 2023 • Huijie Wang, Tianyu Li, Yang Li, Li Chen, Chonghao Sima, Zhenbo Liu, Bangjun Wang, Peijin Jia, Yuting Wang, Shengyin Jiang, Feng Wen, Hang Xu, Ping Luo, Junchi Yan, Wei zhang, Hongyang Li
Accurately depicting the complex traffic scene is a vital component for autonomous vehicles to execute correct judgments.
1 code implementation • 11 Apr 2023 • Tianyu Li, Li Chen, Huijie Wang, Yang Li, Jiazhi Yang, Xiangwei Geng, Shengyin Jiang, Yuting Wang, Hang Xu, Chunjing Xu, Junchi Yan, Ping Luo, Hongyang Li
Understanding the road genome is essential to realize autonomous driving.
Ranked #5 on 3D Lane Detection on OpenLane-V2 val
no code implementations • 2 Dec 2022 • Yuting Wang, Ricardo Guerrero, Vladimir Pavlovic
In its warm-up domain adaptation stage, the model learns a fully-supervised object detector (FSOD) to improve the precision of the object proposals in the target domain, and at the same time learns target-domain-specific and detection-aware proposal features.
1 code implementation • 21 Nov 2022 • Yuting Wang, Jinpeng Wang, Bin Chen, Ziyun Zeng, Shutao Xia
To capture video semantic information for better hashing learning, we adopt an encoder-decoder structure to reconstruct the video from its temporal-masked frames.
no code implementations • 30 Jun 2022 • Yuting Wang, Hangning Zhou, Zhigang Zhang, Chen Feng, Huadong Lin, Chaofei Gao, Yizhi Tang, Zhenting Zhao, Shiyu Zhang, Jie Guo, Xuefeng Wang, Ziyao Xu, Chi Zhang
This technical report presents an effective method for motion prediction in autonomous driving.
Ranked #12 on Motion Forecasting on Argoverse CVPR 2020
1 code implementation • 17 Jul 2020 • Gong-Bo Zhao, Yuting Wang, Atsushi Taruya, Weibing Zhang, Hector Gil-Marin, Arnaud de Mattia, Ashley J. Ross, Anand Raichoor, Cheng Zhao, Will J. Percival, Shadab Alam, Julian E. Bautista, Etienne Burtin, Chia-Hsun Chuang, Kyle S. Dawson, Jean-Paul Kneib, Kazuya Koyama, Helion du Mas des Bourboux, Eva-Maria Mueller, Jeffrey A. Newman, John A. Peacock, Graziano Rossi, Vanina Ruhlmann-Kleider, Donald P. Schneider, Arman Shafieloo
We perform a joint BAO and RSD analysis using the eBOSS DR16 LRG and ELG samples in the redshift range of $z\in[0. 6, 1. 1]$, and detect a RSD signal from the cross power spectrum at a $\sim4\sigma$ confidence level, i. e., $f\sigma_8=0. 317\pm0. 080$ at $z_{\rm eff}=0. 77$.
Cosmology and Nongalactic Astrophysics
1 code implementation • 17 Jul 2020 • Julian E. Bautista, Romain Paviot, Mariana Vargas Magaña, Sylvain de la Torre, Sebastien Fromenteau, Hector Gil-Marín, Ashley J. Ross, Etienne Burtin, Kyle S. Dawson, Jiamin Hou, Jean-Paul Kneib, Arnaud de Mattia, Will J. Percival, Graziano Rossi, Rita Tojeiro, Cheng Zhao, Gong-Bo Zhao, Shadab Alam, Joel Brownstein, Michael J. Chapman, Peter D. Choi, Chia-Hsun Chuang, Stéphanie Escoffier, Axel de la Macorra, Hélion du Mas des Bourboux, Faizan G. Mohammad, Jeongin Moon, Eva-Maria Müller, Seshadri Nadathur, Jeffrey A. Newman, Donald Schneider, Hee-Jong Seo, Yuting Wang
We present the cosmological analysis of the configuration-space anisotropic clustering in the completed Sloan Digital Sky Survey IV (SDSS-IV) extended Baryon Oscillation Spectroscopic Survey (eBOSS) DR16 galaxy sample.
Cosmology and Nongalactic Astrophysics
1 code implementation • 17 Jul 2020 • Yuting Wang, Gong-Bo Zhao, Cheng Zhao, Oliver H. E. Philcox, Shadab Alam, Amélie Tamone, Arnaud de Mattia, Ashley J. Ross, Anand Raichoor, Etienne Burtin, Romain Paviot, Sylvain de la Torre, Will J. Percival, Kyle S. Dawson, Héctor Gil-Marín, Julian E. Bautista, Jiamin Hou, Kazuya Koyama, John A. Peacock, Vanina Ruhlmann-Kleider, Hélion du Mas des Bourboux, Johan Comparat, Stephanie Escoffier, Eva-Maria Mueller, Jeffrey A. Newman, Graziano Rossi, Arman Shafieloo, Donald P. Schneider
We perform a multi-tracer analysis using the complete Sloan Digital Sky Survey IV (SDSS-IV) extended Baryon Oscillation Spectroscopic Survey (eBOSS) DR16 luminous red galaxy (LRG) and the DR16 emission line galaxy (ELG) samples in the configuration space, and successfully detect a cross correlation between the two samples, and find the growth rate to be $f\sigma_8=0. 342 \pm 0. 085$ ($\sim25$ per cent accuracy) from the cross sample alone.
Cosmology and Nongalactic Astrophysics
no code implementations • 29 Jan 2020 • Zuohui Fu, Yikun Xian, Shijie Geng, Yingqiang Ge, Yuting Wang, Xin Dong, Guang Wang, Gerard de Melo
A number of cross-lingual transfer learning approaches based on neural networks have been proposed for the case when large amounts of parallel text are at our disposal.
1 code implementation • ICCV 2019 • Minyoung Kim, Yuting Wang, Pritish Sahu, Vladimir Pavlovic
We propose a family of novel hierarchical Bayesian deep auto-encoder models capable of identifying disentangled factors of variability in data.
1 code implementation • 5 Feb 2019 • Minyoung Kim, Yuting Wang, Pritish Sahu, Vladimir Pavlovic
We propose a novel VAE-based deep auto-encoder model that can learn disentangled latent representations in a fully unsupervised manner, endowed with the ability to identify all meaningful sources of variation and their cardinality.
no code implementations • 21 Mar 2018 • Hai X. Pham, Yuting Wang, Vladimir Pavlovic
This paper presents Generative Adversarial Talking Head (GATH), a novel deep generative neural network that enables fully automatic facial expression synthesis of an arbitrary portrait with continuous action unit (AU) coefficients.
no code implementations • 2 Oct 2017 • Hai X. Pham, Yuting Wang, Vladimir Pavlovic
We present a deep learning framework for real-time speech-driven 3D facial animation from just raw waveforms.
1 code implementation • 15 Sep 2017 • Yuting Wang, Gong-Bo Zhao, Chia-Hsun Chuang, Marcos Pellejero-Ibanez, Cheng Zhao, Francisco-Shu Kitaura, Sergio Rodriguez-Torres
In order to extract the redshift information of anisotropic galaxy clustering, we analyse this data set in nine overlapping redshift slices in configuration space and perform the joint constraints on the parameters $(D_V, F_{\mathrm{AP}}, f\sigma_8)$ using the correlation function multipoles.
Cosmology and Nongalactic Astrophysics
2 code implementations • 11 Jul 2016 • Yuting Wang, Gong-Bo Zhao, Chia-Hsun Chuang, Ashley J. Ross, Will J. Percival, Héctor Gil-Marín, Antonio J. Cuesta, Francisco-Shu Kitaura, Sergio Rodriguez-Torres, Joel R. Brownstein, Daniel J. Eisenstein, Shirley Ho, Jean-Paul Kneib, Matt Olmstead, Francisco Prada, Graziano Rossi, Ariel G. Sánchez, Salvador Salazar-Albornoz, Daniel Thomas, Jeremy Tinker, Rita Tojeiro, Mariana Vargas-Magaña, Fangzhou Zhu
Splitting the sample into multiple overlapping redshift slices to extract the redshift information of galaxy clustering, we obtain a measurement of $D_A(z)/r_d$ and $H(z)r_d$ at nine effective redshifts with the full covariance matrix calibrated using MultiDark-Patchy mock catalogues.
Cosmology and Nongalactic Astrophysics
no code implementations • 11 Jul 2016 • Shadab Alam, Metin Ata, Stephen Bailey, Florian Beutler, Dmitry Bizyaev, Jonathan A. Blazek, Adam S. Bolton, Joel R. Brownstein, Angela Burden, Chia-Hsun Chuang, Johan Comparat, Antonio J. Cuesta, Kyle S. Dawson, Daniel J. Eisenstein, Stephanie Escoffier, Héctor Gil-Marín, Jan Niklas Grieb, Nick Hand, Shirley Ho, Karen Kinemuchi, David Kirkby, Francisco Kitaura, Elena Malanushenko, Viktor Malanushenko, Claudia Maraston, Cameron K. McBride, Robert C. Nichol, Matthew D. Olmstead, Daniel Oravetz, Nikhil Padmanabhan, Nathalie Palanque-Delabrouille, Kaike Pan, Marcos Pellejero-Ibanez, Will J. Percival, Patrick Petitjean, Francisco Prada, Adrian M. Price-Whelan, Beth A. Reid, Sergio A. Rodríguez-Torres, Natalie A. Roe, Ashley J. Ross, Nicholas P. Ross, Graziano Rossi, Jose Alberto Rubiño-Martín, Ariel G. Sánchez, Shun Saito, Salvador Salazar-Albornoz, Lado Samushia, Siddharth Satpathy, Claudia G. Scóccola, David J. Schlegel, Donald P. Schneider, Hee-Jong Seo, Audrey Simmons, Anže Slosar, Michael A. Strauss, Molly E. C. Swanson, Daniel Thomas, Jeremy L. Tinker, Rita Tojeiro, Mariana Vargas Magaña, Jose Alberto Vazquez, Licia Verde, David A. Wake, Yuting Wang, David H. Weinberg, Martin White, W. Michael Wood-Vasey, Christophe Yèche, Idit Zehavi, Zhongxu Zhai, Gong-Bo Zhao
When combined with supernova Ia data, we find H0 = 67. 3+/-1. 0 km/s/Mpc even for our most general dark energy model, in tension with some direct measurements.
Cosmology and Nongalactic Astrophysics