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09/21/2023
Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features
The rapid development of 3D object detection systems for self-driving ca...
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06/05/2023
Discovering Novel Biological Traits From Images Using Phylogeny-Guided Neural Networks
Discovering evolutionary traits that are heritable across species on the...
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05/31/2023
Probabilistic Uncertainty Quantification of Prediction Models with Application to Visual Localization
The uncertainty quantification of prediction models (e.g., neural networ...
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05/26/2023
Towards Open-World Segmentation of Parts
Segmenting object parts such as cup handles and animal bodies is importa...
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05/09/2023
Segment Anything Model (SAM) Enhanced Pseudo Labels for Weakly Supervised Semantic Segmentation
Weakly Supervised Semantic Segmentation (WSSS) with only image-level sup...
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04/16/2023
Federated Learning of Shareable Bases for Personalization-Friendly Image Classification
Personalized federated learning (PFL) aims to harness the collective wis...
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04/13/2023
Unified Out-Of-Distribution Detection: A Model-Specific Perspective
Out-of-distribution (OOD) detection aims to identify test examples that ...
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03/27/2023
Unsupervised Adaptation from Repeated Traversals for Autonomous Driving
For a self-driving car to operate reliably, its perceptual system must g...
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03/12/2023
Making Batch Normalization Great in Federated Deep Learning
Batch Normalization (BN) is commonly used in modern deep neural networks...
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12/08/2022
LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models
This study focuses on embodied agents that can follow natural language i...
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12/06/2022
Visual Query Tuning: Towards Effective Usage of Intermediate Representations for Parameter and Memory Efficient Transfer Learning
Intermediate features of a pre-trained model have been shown informative...
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09/23/2022
Image-to-Image Translation for Autonomous Driving from Coarsely-Aligned Image Pairs
A self-driving car must be able to reliably handle adverse weather condi...
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09/12/2022
PreSTU: Pre-Training for Scene-Text Understanding
The ability to read and reason about texts in an image is often lacking ...
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08/01/2022
Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditions
Advances in perception for self-driving cars have accelerated in recent ...
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07/11/2022
Gradual Domain Adaptation without Indexed Intermediate Domains
The effectiveness of unsupervised domain adaptation degrades when there ...
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06/23/2022
On Pre-Training for Federated Learning
In most of the literature on federated learning (FL), neural networks ar...
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03/29/2022
Learning to Detect Mobile Objects from LiDAR Scans Without Labels
Current 3D object detectors for autonomous driving are almost entirely t...
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03/22/2022
Hindsight is 20/20: Leveraging Past Traversals to Aid 3D Perception
Self-driving cars must detect vehicles, pedestrians, and other traffic p...
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02/14/2022
One Step at a Time: Long-Horizon Vision-and-Language Navigation with Milestones
We study the problem of developing autonomous agents that can follow hum...
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09/24/2021
Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scans
Accurately segmenting teeth and identifying the corresponding anatomical...
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09/20/2021
Sequential Joint Shape and Pose Estimation of Vehicles with Application to Automatic Amodal Segmentation Labeling
Shape and pose estimation is a critical perception problem for a self-dr...
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07/02/2021
On Bridging Generic and Personalized Federated Learning
Federated learning is promising for its ability to collaboratively train...
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07/01/2021
Few-Shot Learning with a Strong Teacher
Few-shot learning (FSL) aims to train a strong classifier using limited ...
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06/30/2021
How to Train Your MAML to Excel in Few-Shot Classification
Model-agnostic meta-learning (MAML) is arguably the most popular meta-le...
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04/21/2021
Revisiting Document Representations for Large-Scale Zero-Shot Learning
Zero-shot learning aims to recognize unseen objects using their semantic...
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04/05/2021
Procrustean Training for Imbalanced Deep Learning
Neural networks trained with class-imbalanced data are known to perform ...
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03/26/2021
Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object Detection
Self-driving cars must detect other vehicles and pedestrians in 3D to pl...
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02/17/2021
A Simple and Effective Use of Object-Centric Images for Long-Tailed Object Detection
Object frequencies in daily scenes follow a long-tailed distribution. Ma...
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09/04/2020
FedDistill: Making Bayesian Model Ensemble Applicable to Federated Learning
Federated learning aims to leverage users' own data and computational re...
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07/24/2020
MiCo: Mixup Co-Training for Semi-Supervised Domain Adaptation
Semi-supervised domain adaptation (SSDA) aims to adapt models from a lab...
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07/06/2020
Wasserstein Distances for Stereo Disparity Estimation
Existing approaches to depth or disparity estimation output a distributi...
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05/17/2020
Train in Germany, Test in The USA: Making 3D Object Detectors Generalize
In the domain of autonomous driving, deep learning has substantially imp...
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04/07/2020
End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection
Reliable and accurate 3D object detection is a necessity for safe autono...
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02/19/2020
Interactive Natural Language-based Person Search
In this work, we consider the problem of searching people in an unconstr...
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02/03/2020
Revisiting Meta-Learning as Supervised Learning
Recent years have witnessed an abundance of new publications and approac...
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01/25/2020
An Empirical Study of Person Re-Identification with Attributes
Person re-identification aims to identify a person from an image collect...
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01/10/2020
Visual Question Answering on 360° Images
In this work, we introduce VQA 360, a novel task of visual question answ...
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01/06/2020
Identifying and Compensating for Feature Deviation in Imbalanced Deep Learning
We investigate learning a ConvNet classifier with class-imbalanced data....
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11/12/2019
SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning
Few-shot learners aim to recognize new object classes based on a small n...
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10/30/2019
LDLS: 3-D Object Segmentation Through Label Diffusion From 2-D Images
Object segmentation in three-dimensional (3-D) point clouds is a critica...
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10/16/2019
A New Defense Against Adversarial Images: Turning a Weakness into a Strength
Natural images are virtually surrounded by low-density misclassified reg...
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07/28/2019
An Empirical Study on Leveraging Scene Graphs for Visual Question Answering
Visual question answering (Visual QA) has attracted significant attentio...
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06/14/2019
Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving
Detecting objects such as cars and pedestrians in 3D plays an indispensa...
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12/18/2018
Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving
3D object detection is an essential task in autonomous driving. Recent t...
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12/16/2018
Classifier and Exemplar Synthesis for Zero-Shot Learning
Zero-shot learning (ZSL) enables solving a task without the need to see ...
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06/10/2018
Cross-Dataset Adaptation for Visual Question Answering
We investigate the problem of cross-dataset adaptation for visual questi...
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06/10/2018
Learning Answer Embeddings for Visual Question Answering
We propose a novel probabilistic model for visual question answering (Vi...
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04/24/2017
Being Negative but Constructively: Lessons Learnt from Creating Better Visual Question Answering Datasets
Visual question answering (QA) has attracted a lot of attention lately, ...
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05/26/2016
Predicting Visual Exemplars of Unseen Classes for Zero-Shot Learning
Leveraging class semantic descriptions and examples of known objects, ze...
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05/26/2016