Results 11 to 20 of about 566,122 (335)
Definitions, 2020 . In this paper we propose a powerful ontology driven method that eases the browsing of any repository of information resources described by an ontology: we provide a flexible semantic granularity method for the navigation of a repository according to ...
Jerry R. Hobbs
semanticscholar +4 more sources
DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems [PDF]
International Conference on Automated Software Engineering, 2018 Deep learning (DL) defines a new data-driven programming paradigm that constructs the internal system logic of a crafted neuron network through a set of training data. We have seen wide adoption of DL in many safety-critical scenarios.
Abadi Martin +12 more
core +2 more sources
Emotional Nuance: Examining Positive Emotional Granularity and Well-Being
Frontiers in Psychology, 2022 The focus of this review is on positive emotional granularity. Emotional granularity is the level of specificity that characterizes verbal representations of an affective experience.
Tse Yen Tan +2 more
doaj +2 more sources
Ferret: Refer and Ground Anything Anywhere at Any Granularity [PDF]
International Conference on Learning Representations, 2023 We introduce Ferret, a new Multimodal Large Language Model (MLLM) capable of understanding spatial referring of any shape or granularity within an image and accurately grounding open-vocabulary descriptions.
Haoxuan You +8 more
semanticscholar +1 more source
Semantic-SAM: Segment and Recognize Anything at Any Granularity [PDF]
arXiv.org, 2023 In this paper, we introduce Semantic-SAM, a universal image segmentation model to enable segment and recognize anything at any desired granularity. Our model offers two key advantages: semantic-awareness and granularity-abundance.
Feng Li +8 more
semanticscholar +1 more source
Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning [PDF]
Neural Information Processing Systems, 2022 Learning medical visual representations directly from paired radiology reports has become an emerging topic in representation learning. However, existing medical image-text joint learning methods are limited by instance or local supervision analysis ...
Fuying Wang +4 more
semanticscholar +1 more source
Learning Granularity-Unified Representations for Text-to-Image Person Re-identification [PDF]
ACM Multimedia, 2022 Text-to-image person re-identification (ReID) aims to search for pedestrian images of an interested identity via textual descriptions. It is challenging due to both rich intra-modal variations and significant inter-modal gaps.
Zhiyin Shao +5 more
semanticscholar +1 more source
The Role of Interoceptive Sensibility and Emotional Conceptualization for the Experience of Emotions
Frontiers in Psychology, 2021 The theory of constructed emotions suggests that different psychological components, including core affect (mental and neural representations of bodily changes), and conceptualization (meaning-making based on prior experiences and semantic knowledge ...
Carlos Ventura-Bort +3 more
doaj +1 more source
UFineBench: Towards Text-based Person Retrieval with Ultra-fine Granularity [PDF]
Computer Vision and Pattern Recognition, 2023 Existing text-based person retrieval datasets often have relatively coarse-grained text annotations. This hinders the model to comprehend the fine-grained semantics of query texts in real scenarios. To address this problem, we con-tribute a new benchmark
Jialong Zuo +7 more
semanticscholar +1 more source
Remote Sensing, 2022 One of the key applications of digital elevation models (DEMs) is cartographic relief presentation. DEMs are widely used in mapping, most commonly in the form of contours, hypsometric tints, and hill shading. Recent advancements in the coverage, quality,
Timofey Samsonov
doaj +1 more source