Jose M. Alvarez

research

∙ 07/05/2022

Object-Level Targeted Selection via Deep Template Matching

Retrieving images with objects that are semantically similar to objects ...

Suraj Kothawade, et al. ∙

research

∙ 07/04/2022

How Much More Data Do I Need? Estimating Requirements for Downstream Tasks

Given a small training data set and a learning algorithm, how much more ...

Rafid Mahmood, et al. ∙

research

∙ 05/30/2022

Knowledge Distillation for 6D Pose Estimation by Keypoint Distribution Alignment

Knowledge distillation facilitates the training of a compact student net...

Shuxuan Guo, et al. ∙

research

∙ 05/08/2022

Non-parametric Depth Distribution Modelling based Depth Inference for Multi-view Stereo

Recent cost volume pyramid based deep neural networks have unlocked the ...

Jiayu Yang, et al. ∙

research

∙ 04/26/2022

Understanding The Robustness in Vision Transformers

Recent studies show that Vision Transformers(ViTs) exhibit strong robust...

Daquan Zhou, et al. ∙

research

∙ 04/11/2022

M^2BEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Birds-Eye View Representation

In this paper, we propose M^2BEV, a unified framework that jointly perfo...

Enze Xie, et al. ∙

research

∙ 02/24/2022

FreeSOLO: Learning to Segment Objects without Annotations

Instance segmentation is a fundamental vision task that aims to recogniz...

Xinlong Wang, et al. ∙

research

∙ 01/27/2022

Fairness implications of encoding protected categorical attributes

Protected attributes are often presented as categorical features that ne...

Carlos Mougan, et al. ∙

research

∙ 11/18/2021

Boosting Supervised Learning Performance with Co-training

Deep learning perception models require a massive amount of labeled trai...

Xinnan Du, et al. ∙

research

∙ 10/22/2021

When to Prune? A Policy towards Early Structural Pruning

Pruning enables appealing reductions in network memory footprint and tim...

Maying Shen, et al. ∙

research

∙ 10/20/2021

HALP: Hardware-Aware Latency Pruning

Structural pruning can simplify network architecture and improve inferen...

Maying Shen, et al. ∙

research

∙ 09/08/2021

Panoptic SegFormer

We present Panoptic SegFormer, a general framework for end-to-end panopt...

Zhiqi Li, et al. ∙

research

∙ 07/13/2021

Deep Neural Networks are Surprisingly Reversible: A Baseline for Zero-Shot Inversion

Understanding the behavior and vulnerability of pre-trained deep neural ...

Xin Dong, et al. ∙

research

∙ 06/22/2021

Towards Reducing Labeling Cost in Deep Object Detection

Deep neural networks have reached very high accuracy on object detection...

Ismail Elezi, et al. ∙

research

∙ 06/09/2021

Distilling Image Classifiers in Object Detectors

Knowledge distillation constitutes a simple yet effective way to improve...

Shuxuan Guo, et al. ∙

research

∙ 05/31/2021

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

We present SegFormer, a simple, efficient yet powerful semantic segmenta...

Enze Xie, et al. ∙

research

∙ 04/15/2021

See through Gradients: Image Batch Recovery via GradInversion

Training deep neural networks requires gradient estimation from data bat...

Hongxu Yin, et al. ∙

research

∙ 04/12/2021

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

Training on datasets with long-tailed distributions has been challenging...

Nadine Chang, et al. ∙

research

∙ 04/07/2021

Self-supervised Learning of Depth Inference for Multi-view Stereo

Recent supervised multi-view depth estimation networks have achieved pro...

Jiayu Yang, et al. ∙

research

∙ 04/06/2021

Contrastive Syn-to-Real Generalization

Training on synthetic data can be beneficial for label or data-scarce sc...

Wuyang Chen, et al. ∙

research

∙ 03/30/2021

Active Learning for Deep Object Detection via Probabilistic Modeling

Active learning aims to reduce labeling costs by selecting only the most...

Jiwoong Choi, et al. ∙

research

∙ 12/15/2020

Personalized Federated Learning with First Order Model Optimization

While federated learning traditionally aims to train a single global mod...

Michael Zhang, et al. ∙

research

∙ 04/09/2020

Scalable Active Learning for Object Detection

Deep Neural Networks trained in a fully supervised fashion are the domin...

Elmar Haussmann, et al. ∙

research

∙ 03/30/2020

Context Based Emotion Recognition using EMOTIC Dataset

In our everyday lives and social interactions we often try to perceive t...

Ronak Kosti, et al. ∙

research

∙ 12/18/2019

Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion

We introduce DeepInversion, a new method for synthesizing images from th...

Hongxu Yin, et al. ∙

research

∙ 12/18/2019

Cost Volume Pyramid Based Depth Inference for Multi-View Stereo

We propose a cost volume based neural network for depth inference from m...

Jiayu Yang, et al. ∙

research

∙ 09/10/2019

VACL: Variance-Aware Cross-Layer Regularization for Pruning Deep Residual Networks

Improving weight sparsity is a common strategy for producing light-weigh...

Shuang Gao, et al. ∙

research

∙ 07/27/2019

Quadtree Generating Networks: Efficient Hierarchical Scene Parsing with Sparse Convolutions

Semantic segmentation with Convolutional Neural Networks is a memory-int...

Kashyap Chitta, et al. ∙

research

∙ 05/29/2019

Less is More: An Exploration of Data Redundancy with Active Dataset Subsampling

Deep Neural Networks (DNNs) often rely on very large datasets for traini...

Kashyap Chitta, et al. ∙

research

∙ 11/29/2018

The Relevance of Bayesian Layer Positioning to Model Uncertainty in Deep Bayesian Active Learning

One of the main challenges of deep learning tools is their inability to ...

Jiaming Zeng, et al. ∙

research

∙ 11/26/2018

ExpandNets: Exploiting Linear Redundancy to Train Small Networks

While very deep networks can achieve great performance, they are ill-sui...

Shuxuan Guo, et al. ∙

research

∙ 11/08/2018

Large-Scale Visual Active Learning with Deep Probabilistic Ensembles

Annotating the right data for training deep neural networks is an import...

Kashyap Chitta, et al. ∙

research

∙ 11/06/2018

Deep Probabilistic Ensembles: Approximate Variational Inference through KL Regularization

In this paper, we introduce Deep Probabilistic Ensembles (DPEs), a scala...

Kashyap Chitta, et al. ∙

research

∙ 07/16/2018

Effective Use of Synthetic Data for Urban Scene Semantic Segmentation

Training a deep network to perform semantic segmentation requires large ...

Fatemeh Sadat Saleh, et al. ∙

research

∙ 11/07/2017

Compression-aware Training of Deep Networks

In recent years, great progress has been made in a variety of applicatio...

Jose M. Alvarez, et al. ∙

research

∙ 09/04/2017

Domain-adaptive deep network compression

Deep Neural Networks trained on large datasets can be easily transferred...

Marc Masana, et al. ∙

research

∙ 08/15/2017

Bringing Background into the Foreground: Making All Classes Equal in Weakly-supervised Video Semantic Segmentation

Pixel-level annotations are expensive and time-consuming to obtain. Henc...

Fatemeh Sadat Saleh, et al. ∙

research

∙ 07/09/2017

Class-Weighted Convolutional Features for Visual Instance Search

Image retrieval in realistic scenarios targets large dynamic datasets of...

Albert Jimenez, et al. ∙

research

∙ 06/06/2017

Incorporating Network Built-in Priors in Weakly-supervised Semantic Segmentation

Pixel-level annotations are expensive and time consuming to obtain. Henc...

Fatemeh Sadat Saleh, et al. ∙

research

∙ 11/19/2016

Invertible Conditional GANs for image editing

Generative Adversarial Networks (GANs) have recently demonstrated to suc...

Guim Perarnau, et al. ∙

research

∙ 11/19/2016

Learning the Number of Neurons in Deep Networks

Nowadays, the number of layers and of neurons in each layer of a deep ne...

Jose M. Alvarez, et al. ∙

research

∙ 09/02/2016

Built-in Foreground/Background Prior for Weakly-Supervised Semantic Segmentation

Pixel-level annotations are expensive and time consuming to obtain. Henc...

Fatemehsadat Saleh, et al. ∙

research

∙ 03/19/2016

Learning Image Matching by Simply Watching Video

This work presents an unsupervised learning based approach to the ubiqui...

Gucan Long, et al. ∙

research

∙ 12/11/2014

Road Detection by One-Class Color Classification: Dataset and Experiments

Detecting traversable road areas ahead a moving vehicle is a key process...

Jose M. Alvarez, et al. ∙

research

∙ 12/10/2014

Road Detection via On--line Label Transfer

Vision-based road detection is an essential functionality for supporting...

Jose M. Alvarez, et al. ∙

Jose M. Alvarez

Featured Co-authors