Cross-Modal Retrieval via Similarity-Preserving Learning and Semantic Average Embedding
نویسندگان
چکیده
منابع مشابه
Cross-Modal Similarity Learning via Pairs, Preferences, and Active Supervision
We present a probabilistic framework for learning pairwise similarities between objects belonging to different modalities, such as drugs and proteins, or text and images. Our framework is based on learning a binary code based representation for objects in each modality, and has the following key properties: (i) it can leverage both pairwise as well as easy-to-obtain relative preference based cr...
متن کاملLearning Deep Semantic Embeddings for Cross-Modal Retrieval
Deep learning methods have been actively researched for cross-modal retrieval, with the softmax cross-entropy loss commonly applied for supervised learning. However, the softmax cross-entropy loss is known to result in large intra-class variances, which is not not very suited for cross-modal matching. In this paper, a deep architecture called Deep Semantic Embedding (DSE) is proposed, which is ...
متن کاملCross-Modal Manifold Learning for Cross-modal Retrieval
This paper presents a new scalable algorithm for cross-modal similarity preserving retrieval in a learnt manifold space. Unlike existing approaches that compromise between preserving global and local geometries, the proposed technique respects both simultaneously during manifold alignment. The global topologies are maintained by recovering underlying mapping functions in the joint manifold spac...
متن کاملCross-Lingual Entity Alignment via Joint Attribute-Preserving Embedding
Entity alignment is the task of finding entities in two knowledge bases (KBs) that represent the same real-world object. When facing KBs in different natural languages, conventional cross-lingual entity alignment methods rely on machine translation to eliminate the language barriers. These approaches often suffer from the uneven quality of translations between languages. While recent embedding-...
متن کاملCross-Media Retrieval via Semantic Entity Projection
Cross-media retrieval is becoming increasingly important nowadays. To address this challenging problem, most existing approaches project heterogeneous features into a unified feature space to facilitate their similarity computation. However, this unified feature space usually has no explicit semantic meanings, which might ignore the hints contained in the original media content, and thus is not...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.3044169