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Found 14 papers, 2 papers with code
DimCL: Dimensional Contrastive Learning For Improving Self-Supervised Learning
Self-supervised learning (SSL) has gained remarkable success, for which contrastive learning (CL) plays a key role.
DPPD: Deformable Polar Polygon Object Detection
We develop a novel Deformable Polar Polygon Object Detection method (DPPD) to detect objects in polygon shapes.
NVAutoNet: Fast and Accurate 360$^{\circ}$ 3D Visual Perception For Self Driving
Achieving robust and real-time 3D perception is fundamental for autonomous vehicles.
On the Pros and Cons of Momentum Encoder in Self-Supervised Visual Representation Learning
Exponential Moving Average (EMA or momentum) is widely used in modern self-supervised learning (SSL) approaches, such as MoCo, for enhancing performance.
Robust MAML: Prioritization task buffer with adaptive learning process for model-agnostic meta-learning
Model agnostic meta-learning (MAML) is a popular state-of-the-art meta-learning algorithm that provides good weight initialization of a model given a variety of learning tasks.
Modality Shifting Attention Network for Multi-modal Video Question Answering
To this end, MSAN is based on (1) the moment proposal network (MPN) that attempts to locate the most appropriate temporal moment from each of the modalities, and also on (2) the heterogeneous reasoning network (HRN) that predicts the answer using an attention mechanism on both modalities.
Efficient Dense Point Cloud Object Reconstruction using Deformation Vector Fields
Given a single image at an arbitrary viewpoint, a CNN predicts multiple surfaces, each in a canonical location relative to the object.
Bayesian Semantic Instance Segmentation in Open Set World
In this paper, we present a novel open-set semantic instance segmentation approach capable of segmenting all known and unknown object classes in images, based on the output of an object detector trained on known object classes.
Structure Aware SLAM using Quadrics and Planes
Simultaneous Localization And Mapping (SLAM) is a fundamental problem in mobile robotics.
Deep-6DPose: Recovering 6D Object Pose from a Single RGB Image
Detecting objects and their 6D poses from only RGB images is an important task for many robotic applications.
Binary Constrained Deep Hashing Network for Image Retrieval without Manual Annotation
However, training deep hashing networks for the task is challenging due to the binary constraints on the hash codes, the similarity preserving property, and the requirement for a vast amount of labelled images.
A Bayesian Data Augmentation Approach for Learning Deep Models
Data augmentation is an essential part of the training process applied to deep learning models.
SceneCut: Joint Geometric and Object Segmentation for Indoor Scenes
This paper presents SceneCut, a novel approach to jointly discover previously unseen objects and non-object surfaces using a single RGB-D image.
The ACRV Picking Benchmark (APB): A Robotic Shelf Picking Benchmark to Foster Reproducible Research
We present a new physical benchmark challenge for robotic picking: the ACRV Picking Benchmark (APB).
