Search Results for author: Yu-Xiong Wang

Found 33 papers, 12 papers with code

Beyond RGB: Scene-Property Synthesis with Neural Radiance Fields

no code implementations9 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.

Data Augmentation Edge Detection +5

Long-Tailed Recognition via Weight Balancing

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.

Classification +1

Discovering Objects that Can Move

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.

Motion Segmentation Object Discovery

The Curse of Zero Task Diversity: On the Failure of Transfer Learning to Outperform MAML and their Empirical Equivalence

no code implementations24 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.

Benchmark Few-Shot Learning +1

Does MAML Only Work via Feature Re-use? A Data Centric Perspective

1 code implementation24 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.

Few-Shot Learning online learning

Embracing Single Stride 3D Object Detector with Sparse Transformer

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.

3D Object Detection Autonomous Driving +2

Generative Modeling for Multitask Visual Learning

no code implementations29 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.

Computer Vision Multi-Task Learning

On the Importance of Distractors for Few-Shot Classification

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.

Classification Contrastive Learning

Generative Modeling for Multi-task Visual Learning

no code implementations25 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.

Computer Vision Multi-Task Learning

Hallucination Improves Few-Shot Object Detection

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.

Benchmark Few-Shot Object Detection +1

Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection

1 code implementation12 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.

DAP: Detection-Aware Pre-training with Weak Supervision

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.

Classification General Classification +4

Cooperating RPN's Improve Few-Shot Object Detection

no code implementations19 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.

Few-Shot Object Detection object-detection +1

Few-Shot Learning with Intra-Class Knowledge Transfer

no code implementations22 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.

Few-Shot Learning Transfer Learning

Alpha Net: Adaptation with Composition in Classifier Space

no code implementations17 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.

Transfer Learning

Bowtie Networks: Generative Modeling for Joint Few-Shot Recognition and Novel-View Synthesis

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.

Data Augmentation Multi-Task Learning +1

Towards Streaming Perception

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.

Instance Segmentation Motion Forecasting +4

Meta-Learning by Hallucinating Useful Examples

no code implementations25 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.

Few-Shot Learning

Growing a Brain: Fine-Tuning by Increasing Model Capacity

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.

Computer Vision Developmental Learning

Learning Compositional Representations for Few-Shot Recognition

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.

Few-Shot Learning

Few-Shot Human Motion Prediction via Meta-Learning

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.

Computer Vision Few-Shot Learning +2

Adversarial Geometry-Aware Human Motion Prediction

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.

Human motion prediction motion prediction

Low-Shot Learning from Imaginary Data

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.

Benchmark General Classification

Learning to Model the Tail

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.

Image Classification Transfer Learning

Learning from Small Sample Sets by Combining Unsupervised Meta-Training with CNNs

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.

Action Recognition General Classification +1

Model Recommendation: Generating Object Detectors From Few Samples

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.

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