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09/11/2023
Towards Viewpoint Robustness in Bird's Eye View Segmentation
Autonomous vehicles (AV) require that neural networks used for perceptio...
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08/04/2023
FB-BEV: BEV Representation from Forward-Backward View Transformations
View Transformation Module (VTM), where transformations happen between m...
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07/28/2023
The Initial Screening Order Problem
In this paper we present the initial screening order problem, a crucial ...
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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...
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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...
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06/09/2023
FasterViT: Fast Vision Transformers with Hierarchical Attention
We design a new family of hybrid CNN-ViT neural networks, named FasterVi...
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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...
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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...
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02/23/2023
Counterfactual Situation Testing: Uncovering Discrimination under Fairness given the Difference
We present counterfactual situation testing (CST), a causal data mining ...
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01/10/2023
Vision Transformers Are Good Mask Auto-Labelers
We propose Mask Auto-Labeler (MAL), a high-quality Transformer-based mas...
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11/04/2022
Soft Masking for Cost-Constrained Channel Pruning
Structured channel pruning has been shown to significantly accelerate in...
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10/13/2022
Structural Pruning via Latency-Saliency Knapsack
Structural pruning can simplify network architecture and improve inferen...
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10/03/2022
Optimizing Data Collection for Machine Learning
Modern deep learning systems require huge data sets to achieve impressiv...
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07/05/2022
Object-Level Targeted Selection via Deep Template Matching
Retrieving images with objects that are semantically similar to objects ...
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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 ...
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05/30/2022
Knowledge Distillation for 6D Pose Estimation by Keypoint Distribution Alignment
Knowledge distillation facilitates the training of a compact student net...
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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 ...
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04/26/2022
Understanding The Robustness in Vision Transformers
Recent studies show that Vision Transformers(ViTs) exhibit strong robust...
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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...
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02/24/2022
FreeSOLO: Learning to Segment Objects without Annotations
Instance segmentation is a fundamental vision task that aims to recogniz...
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01/27/2022
Fairness implications of encoding protected categorical attributes
Protected attributes are often presented as categorical features that ne...
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11/18/2021
Boosting Supervised Learning Performance with Co-training
Deep learning perception models require a massive amount of labeled trai...
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10/22/2021
When to Prune? A Policy towards Early Structural Pruning
Pruning enables appealing reductions in network memory footprint and tim...
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10/20/2021
HALP: Hardware-Aware Latency Pruning
Structural pruning can simplify network architecture and improve inferen...
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09/08/2021
Panoptic SegFormer
We present Panoptic SegFormer, a general framework for end-to-end panopt...
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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 ...
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06/22/2021
Towards Reducing Labeling Cost in Deep Object Detection
Deep neural networks have reached very high accuracy on object detection...
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06/09/2021
Distilling Image Classifiers in Object Detectors
Knowledge distillation constitutes a simple yet effective way to improve...
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05/31/2021
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
We present SegFormer, a simple, efficient yet powerful semantic segmenta...
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04/15/2021
See through Gradients: Image Batch Recovery via GradInversion
Training deep neural networks requires gradient estimation from data bat...
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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...
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04/07/2021
Self-supervised Learning of Depth Inference for Multi-view Stereo
Recent supervised multi-view depth estimation networks have achieved pro...
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04/06/2021
Contrastive Syn-to-Real Generalization
Training on synthetic data can be beneficial for label or data-scarce sc...
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03/30/2021
Active Learning for Deep Object Detection via Probabilistic Modeling
Active learning aims to reduce labeling costs by selecting only the most...
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12/15/2020
Personalized Federated Learning with First Order Model Optimization
While federated learning traditionally aims to train a single global mod...
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04/09/2020
Scalable Active Learning for Object Detection
Deep Neural Networks trained in a fully supervised fashion are the domin...
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03/30/2020
Context Based Emotion Recognition using EMOTIC Dataset
In our everyday lives and social interactions we often try to perceive t...
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12/18/2019
Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion
We introduce DeepInversion, a new method for synthesizing images from th...
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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...
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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...
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07/27/2019
Quadtree Generating Networks: Efficient Hierarchical Scene Parsing with Sparse Convolutions
Semantic segmentation with Convolutional Neural Networks is a memory-int...
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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...
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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 ...
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11/26/2018
ExpandNets: Exploiting Linear Redundancy to Train Small Networks
While very deep networks can achieve great performance, they are ill-sui...
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11/08/2018
Large-Scale Visual Active Learning with Deep Probabilistic Ensembles
Annotating the right data for training deep neural networks is an import...
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11/06/2018
Deep Probabilistic Ensembles: Approximate Variational Inference through KL Regularization
In this paper, we introduce Deep Probabilistic Ensembles (DPEs), a scala...
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07/16/2018
Effective Use of Synthetic Data for Urban Scene Semantic Segmentation
Training a deep network to perform semantic segmentation requires large ...
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11/07/2017
Compression-aware Training of Deep Networks
In recent years, great progress has been made in a variety of applicatio...
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09/04/2017
Domain-adaptive deep network compression
Deep Neural Networks trained on large datasets can be easily transferred...
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08/15/2017
