“ Learning Semantics from Visual Features
نویسنده
چکیده
A system is developed that can learn semantics of words from the visual features extracted from an agent’s perceptual environment. The system is given a static visual scene paired with true statements describing the scene in natural language. When given a novel static scene, the system is able to output the words associated with that scene.
منابع مشابه
Interrogation of a University Classrooms in the Court of Semantics: Managerial Implications
The purpose of this article, within the framework of an interpretive study, was to study the semantics of a universitychr('39')s classrooms to create a critical awareness of the meanings of the symptoms and their functions at the context of physical artifacts, besides their managerial implications. To accomplish this goal, after taking pictures of the structural elements of the studied classroo...
متن کاملبازیابی تعاملی تصاویر طبیعت با بهره گیری از یادگیری چند نمونه ای
Content-based image retrieval (CBIR) has received considerable research interest in the recent years. The basic problem in CBIR is the semantic gap between the high-level image semantics and the low-level image features. Region-based image retrieval and learning from user interaction through relevance feedback are two main approaches to solving this problem. Recently, the research in integra...
متن کاملRecognition of Visual Events using Spatio-Temporal Information of the Video Signal
Recognition of visual events as a video analysis task has become popular in machine learning community. While the traditional approaches for detection of video events have been used for a long time, the recently evolved deep learning based methods have revolutionized this area. They have enabled event recognition systems to achieve detection rates which were not reachable by traditional approac...
متن کاملLearning Multi-level Deep Representations for Image Emotion Classification
In this paper, we propose a new deep network that learns multi-level deep representations for image emotion classification (MldrNet). Image emotion can be recognized through image semantics, image aesthetics and low-level visual features from both global and local views. Existing image emotion classification works using hand-crafted features or deep features mainly focus on either low-level vis...
متن کاملCapturing text semantics for concept detection in news video
The overwhelming amounts of multimedia contents have triggered the need for automatic semantic concept detection. However, as there are large variations in the visual feature space, text from automatic speech recognition (ASR) has been extensively used and found to be effective to complement visual features in the concept detection task. Generally, there are two common text analysis methods. On...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2009