Audio-visual representation learning aims to develop systems with human-...
We show how to build a model that allows realistic, free-viewpoint rende...
Point cloud data plays an essential role in robotics and self-driving
ap...
Temporal consistency is essential for video editing applications. Existi...
In this work, we tackle two vital tasks in automated driving systems, i....
A spatial AI that can perform complex tasks through visual signals and
c...
Deep image prior (DIP) is a recently proposed technique for solving imag...
Age-related macular degeneration (AMD) is the leading cause of visual
im...
3D object detection plays an important role in autonomous driving and ot...
In few-shot imitation learning (FSIL), using behavioral cloning (BC) to ...
A massive number of traffic fatalities are due to driver errors. To redu...
Object detection with multimodal inputs can improve many safety-critical...
Pre-trained representations are becoming crucial for many NLP and percep...
3D object trackers usually require training on large amounts of annotate...
We propose a framework based on causal inference for risk object
identif...
Recent success suggests that deep neural control networks are likely to ...
In this contribution, we design, implement and evaluate the pedagogical
...
To enable intelligent automated driving systems, a promising strategy is...
3D multi-object detection and tracking are crucial for traffic scene
und...
We introduce an unsupervised formulation to estimate heteroscedastic
unc...
We employ triplet loss as a space embedding regularizer to boost
classif...
We cast visual retrieval as a regression problem by posing triplet loss ...
Most work on temporal action detection is formulated in an offline manne...
Driving Scene understanding is a key ingredient for intelligent
transpor...
In this paper, we presented a preliminary study for tactical driver beha...
We present a self-supervised approach using spatio-temporal signals betw...