Despite significant advances in deep learning, models often struggle to
...
Recent implicit neural representations have shown great results for nove...
Producing quality segmentation masks for images is a fundamental problem...
HomeRobot (noun): An affordable compliant robot that navigates homes and...
In this paper, we focus on the important yet understudied problem of
Con...
We present the task of "Social Rearrangement", consisting of cooperative...
A great deal of progress has been made in image captioning, driven by
re...
In this paper, we establish a connection between the parameterization of...
Generalized Category Discovery (GCD) requires a model to both classify k...
In this work, we consider the task of collision-free trajectory planning...
Using multiple spatial modalities has been proven helpful in improving
s...
Recent works demonstrate a remarkable ability to customize text-to-image...
How well do reward functions learned with inverse reinforcement learning...
Recent studies on transfer learning have shown that selectively fine-tun...
We present a single neural network architecture composed of task-agnosti...
This paper addresses the task of joint multi-agent perception and planni...
Computer vision models suffer from a phenomenon known as catastrophic
fo...
In Vision-and-Language Navigation (VLN), researchers typically take an i...
Recently, large-scale pre-trained Vision-and-Language (VL) foundation mo...
Adapting large-scale pretrained models to various downstream tasks via
f...
Federated Learning (FL) seeks to distribute model training across local
...
Recently vision transformers have been shown to be competitive with
conv...
Recent developments for Semi-Supervised Object Detection (SSOD) have sho...
Our method studies the complex task of object-centric 3D understanding f...
With the recent development of Semi-Supervised Object Detection (SS-OD)
...
Significant progress has been made on visual captioning, largely relying...
Continual learning describes a setting where machine learning models lea...
This paper studies the complex task of simultaneous multi-object 3D
reco...
Moving beyond testing on in-distribution data works on Out-of-Distributi...
It is well known that vision classification models suffer from poor
cali...
Reinforcement Learning (RL) is effective in many scenarios. However, it
...
In this paper, we address bandwidth-limited and obstruction-prone
collab...
In this paper, we address the multi-robot collaborative perception probl...
Class imbalance is a fundamental problem in computer vision applications...
We introduce Habitat 2.0 (H2.0), a simulation platform for training virt...
3D point cloud segmentation is an important function that helps robots
u...
Semi-supervised learning, i.e., training networks with both labeled and
...
Neural Networks can perform poorly when the training label distribution ...
3D object detection is a core perceptual challenge for robotics and
auto...
Recent state-of-the-art semi-supervised learning (SSL) methods use a
com...
Convolutional Neural Networks (CNNs) show impressive performance in the
...
While significant advances have been made for single-agent perception, m...
In this paper, we propose the problem of collaborative perception, where...
Deep neural networks have attained remarkable performance when applied t...
The fusion of multiple sensor modalities, especially through deep learni...
Although various image-based domain adaptation (DA) techniques have been...
Recent advances in semi-supervised learning methods rely on estimating
c...
When generating a sentence description for an image, it frequently remai...
Prior work has shown that the multi-relational embedding objective can b...
The knowledge base completion problem is the problem of inferring missin...