Results 71 to 80 of about 1,105,657 (336)
Regionlets for Generic Object Detection [PDF]
Generic object detection is confronted by dealing with different degrees of variations, caused by viewpoints or deformations in distinct object classes, with tractable computations. This demands for descriptive and flexible object representations which can be efficiently evaluated in many locations.
Ming Yang +3 more
openaire +4 more sources
Population size and dynamics fundamentally shape speciation by influencing genetic drift, founder events, and adaptive potential. Small populations may speciate rapidly due to stronger drift, whereas large populations harbor more genetic diversity, which can alter divergence trajectories. We highlight theoretical models that incorporate population size
Ryo Yamaguchi +3 more
wiley +1 more source
Recently, deep Convolutional Neural Networks (CNN) have demonstrated strong performance on RGB salient object detection. Although, depth information can help improve detection results, the exploration of CNNs for RGB-D salient object detection remains ...
Barnes, Nick +3 more
core +1 more source
Described Object Detection: Liberating Object Detection with Flexible Expressions
Detecting objects based on language information is a popular task that includes Open-Vocabulary object Detection (OVD) and Referring Expression Comprehension (REC). In this paper, we advance them to a more practical setting called Described Object Detection (DOD) by expanding category names to flexible language expressions for OVD and overcoming the ...
Xie, Chi +5 more
openaire +2 more sources
Evolutionary interplay between viruses and R‐loops
Viruses interact with specialized nucleic acid structures called R‐loops to influence host transcription, epigenetic states, latency, and immune evasion. This Perspective examines the roles of R‐loops in viral replication, integration, and silencing, and how viruses co‐opt or avoid these structures.
Zsolt Karányi +4 more
wiley +1 more source
A Survey of Zero-Shot Object Detection
Zero-Shot object Detection (ZSD), one of the most challenging problems in the field of object detection, aims to accurately identify new categories that are not encountered during training. Recent advancements in deep learning and increased computational
Weipeng Cao +5 more
doaj +1 more source
Review of One-Stage Universal Object Detection Algorithms in Deep Learning [PDF]
In recent years, object detection algorithms have gradually become a hot research direction as a core task in the field of computer vision. They enable computers to recognize and locate target objects in images or video frames, and are widely used in ...
WANG Ning, ZHI Min
doaj +1 more source
Centered Multi-Task Generative Adversarial Network for Small Object Detection
Despite the breakthroughs in accuracy and efficiency of object detection using deep neural networks, the performance of small object detection is far from satisfactory. Gaze estimation has developed significantly due to the development of visual sensors.
Hongfeng Wang +3 more
doaj +1 more source
Object-Detecting Neurons in Drosophila [PDF]
Many animals rely on vision to detect objects such as conspecifics, predators, and prey. Hypercomplex cells found in feline cortex and small target motion detectors found in dragonfly and hoverfly optic lobes demonstrate robust tuning for small objects, with weak or no response to larger objects or movement of the visual panorama [1-3].
Keleş, Mehmet F, Frye, Mark A
openaire +5 more sources
Circulating histones as clinical biomarkers in critically ill conditions
Circulating histones are emerging as promising biomarkers in critical illness due to their diagnostic, prognostic, and therapeutic potential. Detection methods such as ELISA and mass spectrometry provide reliable approaches for quantifying histone levels in plasma samples.
José Luis García‐Gimenez +17 more
wiley +1 more source

