179 research outputs found
The M33 Synoptic Stellar Survey. II. Mira Variables
We present the discovery of 1847 Mira candidates in the Local Group galaxy
M33 using a novel semi-parametric periodogram technique coupled with a Random
Forest classifier. The algorithms were applied to ~2.4x10^5 I-band light curves
previously obtained by the M33 Synoptic Stellar Survey. We derive preliminary
Period-Luminosity relations at optical, near- & mid-infrared wavelengths and
compare them to the corresponding relations in the Large Magellanic Cloud.Comment: Includes small corrections to match the published versio
Recommended from our members
A General Framework for Model Adaptation to Meet Practical Constraints in Computer Vision
Recent advances in deep learning models have shown impressive capabilities in various computer vision tasks, which encourages the integration of these models into real-world vision systems such as smart devices. This integration presents new challenges as models need to meet complex real-world requirements. This thesis is dedicated to building practical deep learning models, where we focus on two main challenges in vision systems: data efficiency and variability. We address these issues by providing a general model adaptation framework that extends models with practical capabilities.
In the first part of the thesis, we explore model adaptation approaches for efficient representation. We illustrate the benefits of different types of efficient data representations, including compressed video modalities from video codecs, low-bit features and sparsified frames and texts. By using such efficient representation, the system complexity such as data storage, processing and computation can be greatly reduced. We systematically study various methods to extract, learn and utilize these representations, presenting new methods to adapt machine learning models for them. The proposed methods include a compressed-domain video recognition model with coarse-to-fine distillation training strategy, a task-specific feature compression framework for low-bit video-and-language understanding, and a learnable token sparsification approach for sparsifying human-interpretable video inputs. We demonstrate new perspectives of representing vision data in a more practical and efficient way in various applications.
The second part of the thesis focuses on open environment challenges, where we explore model adaptation for new, unseen classes and domains. We examine the practical limitations in current recognition models, and introduce various methods to empower models in addressing open recognition scenarios. This includes a negative envisioning framework for managing new classes and outliers, and a multi-domain translation approach for dealing with unseen domain data. Our study shows a promising trajectory towards models exhibiting the capability to navigate through diverse data environments in real-world applications
Task-Adaptive Negative Class Envision for Few-Shot Open-Set Recognition
Recent works seek to endow recognition systems with the ability to handle the
open world. Few shot learning aims for fast learning of new classes from
limited examples, while open-set recognition considers unknown negative class
from the open world. In this paper, we study the problem of few-shot open-set
recognition (FSOR), which learns a recognition system robust to queries from
new sources with few examples and from unknown open sources. To achieve that,
we mimic human capability of envisioning new concepts from prior knowledge, and
propose a novel task-adaptive negative class envision method (TANE) to model
the open world. Essentially we use an external memory to estimate a negative
class representation. Moreover, we introduce a novel conjugate episode training
strategy that strengthens the learning process. Extensive experiments on four
public benchmarks show that our approach significantly improves the
state-of-the-art performance on few-shot open-set recognition. Besides, we
extend our method to generalized few-shot open-set recognition (GFSOR), where
we also achieve performance gains on MiniImageNet
- …