Jose M. Alvarez

research

∙ 09/11/2023

Towards Viewpoint Robustness in Bird's Eye View Segmentation

Autonomous vehicles (AV) require that neural networks used for perceptio...

0 Tzofi Klinghoffer, et al. ∙

research

∙ 08/04/2023

FB-BEV: BEV Representation from Forward-Backward View Transformations

View Transformation Module (VTM), where transformations happen between m...

0 Zhiqi Li, et al. ∙

research

∙ 07/28/2023

The Initial Screening Order Problem

In this paper we present the initial screening order problem, a crucial ...

0 Jose M. Alvarez, et al. ∙

research

∙ 07/09/2023

Parametric Depth Based Feature Representation Learning for Object Detection and Segmentation in Bird's Eye View

Recent vision-only perception models for autonomous driving achieved pro...

0 Jiayu Yang, et al. ∙

research

∙ 07/04/2023

FB-OCC: 3D Occupancy Prediction based on Forward-Backward View Transformation

This technical report summarizes the winning solution for the 3D Occupan...

0 Zhiqi Li, et al. ∙

research

∙ 06/09/2023

FasterViT: Fast Vision Transformers with Hierarchical Attention

We design a new family of hybrid CNN-ViT neural networks, named FasterVi...

0 Ali Hatamizadeh, et al. ∙

research

∙ 02/27/2023

Domain Adaptive Decision Trees: Implications for Accuracy and Fairness

In uses of pre-trained machine learning models, it is a known issue that...

0 Jose M. Alvarez, et al. ∙

research

∙ 02/23/2023

VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completion

Humans can easily imagine the complete 3D geometry of occluded objects a...

11 Yiming Li, et al. ∙

research

∙ 02/23/2023

Counterfactual Situation Testing: Uncovering Discrimination under Fairness given the Difference

We present counterfactual situation testing (CST), a causal data mining ...

0 Jose M. Alvarez, et al. ∙

research

∙ 01/10/2023

Vision Transformers Are Good Mask Auto-Labelers

We propose Mask Auto-Labeler (MAL), a high-quality Transformer-based mas...

13 Shiyi Lan, et al. ∙

research

∙ 11/04/2022

Soft Masking for Cost-Constrained Channel Pruning

Structured channel pruning has been shown to significantly accelerate in...

0 Ryan Humble, et al. ∙

research

∙ 10/13/2022

Structural Pruning via Latency-Saliency Knapsack

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

0 Maying Shen, et al. ∙

research

∙ 10/03/2022

Optimizing Data Collection for Machine Learning

Modern deep learning systems require huge data sets to achieve impressiv...

54 Rafid Mahmood, et al. ∙

research

∙ 07/05/2022

Object-Level Targeted Selection via Deep Template Matching

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

0 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 ...

63 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...

0 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 ...

0 Jiayu Yang, et al. ∙

research

∙ 04/26/2022

Understanding The Robustness in Vision Transformers

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

13 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...

17 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...

59 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...

5 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...

0 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...

0 Maying Shen, et al. ∙

research

∙ 10/20/2021

HALP: Hardware-Aware Latency Pruning

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

0 Maying Shen, et al. ∙

research

∙ 09/08/2021

Panoptic SegFormer

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

8 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 ...

8 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...

7 Ismail Elezi, et al. ∙

research

∙ 06/09/2021

Distilling Image Classifiers in Object Detectors

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

0 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...

6 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...

0 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...

27 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...

4 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...

7 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...

0 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...

17 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...

13 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...

2 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...

27 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...

18 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...

0 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...

3 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...

0 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 ...

0 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...

0 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...

0 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...

0 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 ...

0 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...

0 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...

0 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...

0 Fatemeh Sadat Saleh, et al. ∙

Jose M. Alvarez

Featured Co-authors