no code implementations • 10 Jun 2022 • Yuanyi Zhong, Haoran Tang, Junkun Chen, Jian Peng, Yu-Xiong Wang
Our insight has implications in improving the downstream robustness of supervised learning.
no code implementations • 9 Jun 2022 • Mingtong Zhang, Shuhong Zheng, Zhipeng Bao, Martial Hebert, Yu-Xiong Wang
Comprehensive 3D scene understanding, both geometrically and semantically, is important for real-world applications such as robot perception.
1 code implementation • CVPR 2022 • Shaden Alshammari, Yu-Xiong Wang, Deva Ramanan, Shu Kong
In contrast, weight decay penalizes larger weights more heavily and so learns small balanced weights; the MaxNorm constraint encourages growing small weights within a norm ball but caps all the weights by the radius.
Ranked #1 on
Long-tail Learning
on CIFAR-100-LT (ρ=50)
1 code implementation • CVPR 2022 • Zhipeng Bao, Pavel Tokmakov, Allan Jabri, Yu-Xiong Wang, Adrien Gaidon, Martial Hebert
Our experiments demonstrate that, despite only capturing a small subset of the objects that move, this signal is enough to generalize to segment both moving and static instances of dynamic objects.
no code implementations • 24 Dec 2021 • Brando Miranda, Yu-Xiong Wang, Sanmi Koyejo
We hypothesize that the diversity coefficient of the few-shot learning benchmark is predictive of whether meta-learning solutions will succeed or not.
1 code implementation • 24 Dec 2021 • Brando Miranda, Yu-Xiong Wang, Sanmi Koyejo
Recent work has suggested that a good embedding is all we need to solve many few-shot learning benchmarks.
1 code implementation • CVPR 2022 • Lue Fan, Ziqi Pang, Tianyuan Zhang, Yu-Xiong Wang, Hang Zhao, Feng Wang, Naiyan Wang, Zhaoxiang Zhang
In LiDAR-based 3D object detection for autonomous driving, the ratio of the object size to input scene size is significantly smaller compared to 2D detection cases.
Ranked #1 on
3D Object Detection
on waymo pedestrian
1 code implementation • ICLR 2022 • Saba Ghaffari, Ehsan Saleh, David Forsyth, Yu-Xiong Wang
In this work, we demonstrate the effectiveness of Firth bias reduction in few-shot classification.
no code implementations • 29 Sep 2021 • Zhipeng Bao, Yu-Xiong Wang, Martial Hebert
Generative modeling has recently shown great promise in computer vision, but it has mostly focused on synthesizing visually realistic images.
1 code implementation • ICCV 2021 • Rajshekhar Das, Yu-Xiong Wang, JoséM. F. Moura
An effective approach to few-shot classification involves a prior model trained on a large-sample base domain, which is then finetuned over the novel few-shot task to yield generalizable representations.
no code implementations • ICCV 2021 • Yuanyi Zhong, Bodi Yuan, Hong Wu, Zhiqiang Yuan, Jian Peng, Yu-Xiong Wang
We leverage the pixel-level L2 loss and the pixel contrastive loss for the two purposes respectively.
no code implementations • 25 Jun 2021 • Zhipeng Bao, Martial Hebert, Yu-Xiong Wang
Generative modeling has recently shown great promise in computer vision, but it has mostly focused on synthesizing visually realistic images.
no code implementations • CVPR 2021 • Weilin Zhang, Yu-Xiong Wang
One critical factor in improving few-shot detection is to address the lack of variation in training data.
1 code implementation • 12 Apr 2021 • Nadine Chang, Zhiding Yu, Yu-Xiong Wang, Anima Anandkumar, Sanja Fidler, Jose M. Alvarez
As a result, image resampling alone is not enough to yield a sufficiently balanced distribution at the object level.
1 code implementation • CVPR 2021 • Yuanyi Zhong, JianFeng Wang, Lijuan Wang, Jian Peng, Yu-Xiong Wang, Lei Zhang
This paper presents a detection-aware pre-training (DAP) approach, which leverages only weakly-labeled classification-style datasets (e. g., ImageNet) for pre-training, but is specifically tailored to benefit object detection tasks.
no code implementations • ICCV 2021 • Liangke Gui, Adrien Bardes, Ruslan Salakhutdinov, Alexander Hauptmann, Martial Hebert, Yu-Xiong Wang
Learning to hallucinate additional examples has recently been shown as a promising direction to address few-shot learning tasks.
no code implementations • 19 Nov 2020 • Weilin Zhang, Yu-Xiong Wang, David A. Forsyth
Learning to detect an object in an image from very few training examples - few-shot object detection - is challenging, because the classifier that sees proposal boxes has very little training data.
no code implementations • 22 Aug 2020 • Vivek Roy, Yan Xu, Yu-Xiong Wang, Kris Kitani, Ruslan Salakhutdinov, Martial Hebert
Recent works have proposed to solve this task by augmenting the training data of the few-shot classes using generative models with the few-shot training samples as the seeds.
no code implementations • 17 Aug 2020 • Nadine Chang, Jayanth Koushik, Michael J. Tarr, Martial Hebert, Yu-Xiong Wang
Motivated by the human ability to solve this task, models have been developed that transfer knowledge from classes with many examples to learn classes with few examples.
no code implementations • ICLR 2021 • Zhipeng Bao, Yu-Xiong Wang, Martial Hebert
We propose a novel task of joint few-shot recognition and novel-view synthesis: given only one or few images of a novel object from arbitrary views with only category annotation, we aim to simultaneously learn an object classifier and generate images of that type of object from new viewpoints.
1 code implementation • ECCV 2020 • Mengtian Li, Yu-Xiong Wang, Deva Ramanan
While past work has studied the algorithmic trade-off between latency and accuracy, there has not been a clear metric to compare different methods along the Pareto optimal latency-accuracy curve.
Ranked #2 on
Real-Time Object Detection
on Argoverse-HD (Detection-Only, Val)
(using extra training data)
1 code implementation • 29 Nov 2019 • Ziqi Pang, Zhiyuan Hu, Pavel Tokmakov, Yu-Xiong Wang, Martial Hebert
Indeed, even the majority of few-shot learning methods rely on a large set of "base classes" for pretraining.
no code implementations • ICCV 2019 • Yu-Xiong Wang, Deva Ramanan, Martial Hebert
Few-shot learning, i. e., learning novel concepts from few examples, is fundamental to practical visual recognition systems.
Ranked #14 on
Few-Shot Object Detection
on MS-COCO (30-shot)
no code implementations • 25 Sep 2019 • Yu-Xiong Wang, Yuki Uchiyama, Martial Hebert, Karteek Alahari
Learning to hallucinate additional examples has recently been shown as a promising direction to address few-shot learning tasks, which aim to learn novel concepts from very few examples.
no code implementations • CVPR 2017 • Yu-Xiong Wang, Deva Ramanan, Martial Hebert
One of their remarkable properties is the ability to transfer knowledge from a large source dataset to a (typically smaller) target dataset.
1 code implementation • CVPR 2019 • Zitian Chen, Yanwei Fu, Yu-Xiong Wang, Lin Ma, Wei Liu, Martial Hebert
Humans can robustly learn novel visual concepts even when images undergo various deformations and lose certain information.
no code implementations • ICCV 2019 • Pavel Tokmakov, Yu-Xiong Wang, Martial Hebert
One of the key limitations of modern deep learning approaches lies in the amount of data required to train them.
no code implementations • ECCV 2018 • Liang-Yan Gui, Yu-Xiong Wang, Deva Ramanan, Jose M. F. Moura
This paper addresses the problem of few-shot human motion prediction, in the spirit of the recent progress on few-shot learning and meta-learning.
no code implementations • ECCV 2018 • Liang-Yan Gui, Yu-Xiong Wang, Xiaodan Liang, Jose M. F. Moura
We explore an approach to forecasting human motion in a few milliseconds given an input 3D skeleton sequence based on a recurrent encoder-decoder framework.
1 code implementation • CVPR 2018 • Yu-Xiong Wang, Ross Girshick, Martial Hebert, Bharath Hariharan
Humans can quickly learn new visual concepts, perhaps because they can easily visualize or imagine what novel objects look like from different views.
no code implementations • NeurIPS 2017 • Yu-Xiong Wang, Deva Ramanan, Martial Hebert
We cast this problem as transfer learning, where knowledge from the data-rich classes in the head of the distribution is transferred to the data-poor classes in the tail.
no code implementations • NeurIPS 2016 • Yu-Xiong Wang, Martial Hebert
Inspired by the transferability properties of CNNs, we introduce an additional unsupervised meta-training stage that exposes multiple top layer units to a large amount of unlabeled real-world images.
no code implementations • CVPR 2015 • Yu-Xiong Wang, Martial Hebert
In this paper, we explore an approach to generating detectors that is radically different from the conventional way of learning a detector from a large corpus of annotated positive and negative data samples.