semantic object
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Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 337
Author(s):  
Hatem Ibrahem ◽  
Ahmed Salem ◽  
Hyun-Soo Kang

We propose Depth-to-Space Net (DTS-Net), an effective technique for semantic segmentation using the efficient sub-pixel convolutional neural network. This technique is inspired by depth-to-space (DTS) image reconstruction, which was originally used for image and video super-resolution tasks, combined with a mask enhancement filtration technique based on multi-label classification, namely, Nearest Label Filtration. In the proposed technique, we employ depth-wise separable convolution-based architectures. We propose both a deep network, that is, DTS-Net, and a lightweight network, DTS-Net-Lite, for real-time semantic segmentation; these networks employ Xception and MobileNetV2 architectures as the feature extractors, respectively. In addition, we explore the joint semantic segmentation and depth estimation task and demonstrate that the proposed technique can efficiently perform both tasks simultaneously, outperforming state-of-art (SOTA) methods. We train and evaluate the performance of the proposed method on the PASCAL VOC2012, NYUV2, and CITYSCAPES benchmarks. Hence, we obtain high mean intersection over union (mIOU) and mean pixel accuracy (Pix.acc.) values using simple and lightweight convolutional neural network architectures of the developed networks. Notably, the proposed method outperforms SOTA methods that depend on encoder–decoder architectures, although our implementation and computations are far simpler.


2021 ◽  
pp. 026540752110504
Author(s):  
Pranav Malhotra ◽  
Kristina Scharp ◽  
Lindsey Thomas

What misinformation means and what it means to be someone who corrects it is socially contested, especially in interpersonal contexts where politeness expectations complicate correction. Given this flux in meaning, we analyze posts about misinformation correction in interpersonal contexts from the AmItheAsshole subreddit through a relational dialectics theory (RDT) lens. Findings revealed that discourses of misinformation as harmful and as innocuous and potentially helpful constituted the meaning of misinformation, while discourses of misinformation correctors as inconsiderate and as communal guardians constituted the meaning of misinformation correctors. The latter meaning was dependent on the meaning of misinformation and the adjacent ideology of politeness. Thus, we extend RDT by elucidating how the meaning of a semantic object is predicated on a web of larger intertextual meaning.


2021 ◽  
Vol 63 ◽  
pp. e021042
Author(s):  
Andrés Saab

Within the framework of a uniform theory of the so-called se constructions in Spanish, I propose to explain a control ban that has received almost no attention in the previous bibliography. Specifically, as long as a subject control sentence has an impersonal SE as controller, the subordinate infinitive clause cannot contain any other instance of the clitic SE, other than the so-called spurious SE. The source of this restriction follows, as I will argue, from a legibility problem at LF produced, specifically, by a failed attempt to apply Agree between PRO and the embedded SE, which, as we shall see, acts as a probe for A-movement. If the explanation that I offer is correct, it also follows a series of theoretical conclusions that directly affect the way in which we must conceive of the design of Agree in the syntax and its effect at the LF interface. In particular, the system tolerates certain Agree failures (Preminger 2014) as long as it does not affect legibility in the semantics. Indeed, the theory of SE constructions that I assume here derives the distinction between paradigmatic and non-paradigmatic SE as the result of successful or unsuccessful Agree applications, respectively. The limit of this tolerance to failed applications of Agree must be found in the type of semantic object that can be deduced at LF. This limit is illustrated with the aforementioned restriction in control and impersonal SE contexts that motivates the present study.


Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7888
Author(s):  
Li-Yu Lo ◽  
Chi Hao Yiu ◽  
Yu Tang ◽  
An-Shik Yang ◽  
Boyang Li ◽  
...  

The ever-burgeoning growth of autonomous unmanned aerial vehicles (UAVs) has demonstrated a promising platform for utilization in real-world applications. In particular, a UAV equipped with a vision system could be leveraged for surveillance applications. This paper proposes a learning-based UAV system for achieving autonomous surveillance, in which the UAV can be of assistance in autonomously detecting, tracking, and following a target object without human intervention. Specifically, we adopted the YOLOv4-Tiny algorithm for semantic object detection and then consolidated it with a 3D object pose estimation method and Kalman filter to enhance the perception performance. In addition, UAV path planning for a surveillance maneuver is integrated to complete the fully autonomous system. The perception module is assessed on a quadrotor UAV, while the whole system is validated through flight experiments. The experiment results verified the robustness, effectiveness, and reliability of the autonomous object tracking UAV system in performing surveillance tasks. The source code is released to the research community for future reference.


2021 ◽  
Author(s):  
Alexander Enge ◽  
Rasha Abdel Rahman ◽  
Michael A. Skeide

Our capacity to derive meaning from things that we see and words that we hear is unparalleled in other animal species and current AI systems. Despite a wealth of functional magnetic resonance imaging (fMRI) studies on where different semantic features are processed in the adult brain, the development of these systems in children is poorly understood. Here we conducted an extensive database search and identified 50 fMRI experiments investigating semantic world knowledge, semantic relatedness judgments, and the differentiation of visual semantic object categories in children (total N = 1,018, mean age = 10.1 years, range 4-15 years). Synthesizing the results of these experiments, we found consistent activation in the bilateral inferior frontal gyri (IFG), fusiform gyri (FG), and supplementary motor areas (SMA), as well as in the left middle and superior temporal gyri (MTG/STG). Within this system, we found little evidence for age-related changes across childhood and high overlap with the adult semantic system. In sum, the identification of these cortical areas provides the starting point for further research on the mechanisms by which the developing brain learns to make sense of its environment.


Author(s):  
Pascal Mettes ◽  
William Thong ◽  
Cees G. M. Snoek

AbstractThis work strives for the classification and localization of human actions in videos, without the need for any labeled video training examples. Where existing work relies on transferring global attribute or object information from seen to unseen action videos, we seek to classify and spatio-temporally localize unseen actions in videos from image-based object information only. We propose three spatial object priors, which encode local person and object detectors along with their spatial relations. On top we introduce three semantic object priors, which extend semantic matching through word embeddings with three simple functions that tackle semantic ambiguity, object discrimination, and object naming. A video embedding combines the spatial and semantic object priors. It enables us to introduce a new video retrieval task that retrieves action tubes in video collections based on user-specified objects, spatial relations, and object size. Experimental evaluation on five action datasets shows the importance of spatial and semantic object priors for unseen actions. We find that persons and objects have preferred spatial relations that benefit unseen action localization, while using multiple languages and simple object filtering directly improves semantic matching, leading to state-of-the-art results for both unseen action classification and localization.


2021 ◽  
Author(s):  
Andrew Holliday ◽  
Gregory Dudek

AbstractThis work presents Object Landmarks, a new type of visual feature designed for visual localization over major changes in distance and scale. An Object Landmark consists of a bounding box $${\mathbf {b}}$$ b defining an object, a descriptor $${\mathbf {q}}$$ q of that object produced by a Convolutional Neural Network, and a set of classical point features within $${\mathbf {b}}$$ b . We evaluate Object Landmarks on visual odometry and place-recognition tasks, and compare them against several modern approaches. We find that Object Landmarks enable superior localization over major scale changes, reducing error by as much as 18% and increasing robustness to failure by as much as 80% versus the state-of-the-art. They allow localization under scale change factors up to 6, where state-of-the-art approaches break down at factors of 3 or more.


2021 ◽  
Author(s):  
Lei He ◽  
Jiwen Lu ◽  
Guanghui Wang ◽  
Shiyu Song ◽  
Jie Zhou

Author(s):  
Francesco Ragusa ◽  
Daniele Di Mauro ◽  
Alfio Palermo ◽  
Antonino Furnari ◽  
Giovanni Maria Farinella

Author(s):  
Noam Khayat ◽  
Stefano Fusi ◽  
Shaul Hochstein

AbstractPerception, representation, and memory of ensemble statistics has attracted growing interest. Studies found that, at different abstraction levels, the brain represents similar items as unified percepts. We found that global ensemble perception is automatic and unconscious, affecting later perceptual judgments regarding individual member items. Implicit effects of set mean and range for low-level feature ensembles (size, orientation, brightness) were replicated for high-level category objects. This similarity suggests that analogous mechanisms underlie these extreme levels of abstraction. Here, we bridge the span between visual features and semantic object categories using the identical implicit perception experimental paradigm for intermediate novel visual-shape categories, constructing ensemble exemplars by introducing systematic variations of a central category base or ancestor. In five experiments, with different item variability, we test automatic representation of ensemble category characteristics and its effect on a subsequent memory task. Results show that observer representation of ensembles includes the group’s central shape, category ancestor (progenitor), or group mean. Observers also easily reject memory of shapes belonging to different categories, i.e. originating from different ancestors. We conclude that complex categories, like simple visual form ensembles, are represented in terms of statistics including a central object, as well as category boundaries. We refer to the model proposed by Benna and Fusi (bioRxiv 624239, 2019) that memory representation is compressed when related elements are represented by identifying their ancestor and each one’s difference from it. We suggest that ensemble mean perception, like category prototype extraction, might reflect employment at different representation levels of an essential, general representation mechanism.


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