Visible-infrared person re-identification (VI-ReID) is undoubtedly a challenging cross-modality person retrieval task with increasing appreciation. Compared to traditional person ReID that focuses on person images in a single RGB mode, VI-ReID suffers ...

Recent studies have verified that learning compact hash codes can facilitate big data retrieval processing. In particular, learning the deep hash function can greatly improve the retrieval performance. However, the existing deep supervised hashing ...

This study is aimed at finding a suitable method for generating time-series data such as video clips or avatar motions from text stating multiple events. This paper addresses the generation of variable-length time-series data considering the order and ...

Multimodal social event detection has been attracting tremendous research attention in recent years, due to that it provides comprehensive and complementary understanding of social events and is important to public security and administration. Most ...

With the emergence of various online platforms, associating different platforms is playing an increasingly important role in many applications. Cross-platform recommendation aims to improve recommendation accuracy through associating information from ...

Social events are very common activities, where people can interact with each other. During an event, the organizer often hires photographers to take images, which provide rich information about the participants’ behaviour. In this work, we propose a ...

Efficient retrieval of score information from a large set of XML-encoded scores and lyrics in an XML database requires such music data to be stored in a well-structured and systematic technique. Current search engines for Indic music (Tagore songs in ...

The Deep Video Understanding Challenge (DVU) is a task that focuses on comprehending long duration videos which involve many entities. Its main goal is to build relationship and interaction knowledge graph between entities to answer relevant questions. ...

Prior hashing works typically learn a projection function from high-dimensional visual feature space to low-dimensional latent space. However, such a projection function remains several crucial bottlenecks: 1) information loss and coding redundancy are ...

Building on the recent advances in multimodal zero-shot representation learning, in this paper we explore the use of features obtained from the recent CLIP model to perform conditioned image retrieval. Starting from a reference image and an additive ...

Hashing can compress heterogeneous high-dimensional data into compact binary codes. For most existing hash methods, they first predetermine a fixed length for the hash code and then train the model based on this fixed length. However, when the task ...

With the rapid development of intelligent detection algorithms based on deep learning, much progress has been made in automatic road defect recognition and road marking parsing. This can effectively address the issue of an expensive and time-consuming ...

The various data and privacy regulations introduced around the globe, require data to be stored in a secure and privacy-preserving fashion. Non-compliance with these regulations come with major consequences. This has led to the formation of huge data ...

We propose an efficient bus crowdedness classification system that can be used in daily life. In particular, we analyze and study the data collected from real bus, aiming to deal with the difficulty of bus congestion classification. Besides, we combine ...

In this paper, we are interested in understanding how customers perceive fashion recommendations, in particular when observing a proposed combination of garments to compose an outfit. Automatically understanding how a suggested item is perceived, ...

The paradigm shift in the consumption of news via online platforms has cultivated the growth of digital journalism. Contrary to traditional media, lowering entry barriers and enabling everyone to be part of content creation have disabled the concept of ...

Zero-shot learning (ZSL) aims to recognize images from unseen (novel) classes with the training images from seen classes. The attributes of each class is exploited as auxiliary semantic information. Recently most ZSL approaches focus on learning visual-...

In this paper, we explore the tokenized representation of musical scores using the Transformer model to automatically generate musical scores. Thus far, sequence models have yielded fruitful results with note-level (MIDI-equivalent) symbolic ...

Temporal Sentence Grounding aims to localize the relevant temporal region in a given video according to the query sentence. It is a challenging task due to the semantic gap between different modalities and diversity of the event duration. Proposal ...

Visual storytelling, which aims at automatically producing a narrative paragraph for photo album, remains quite challenging due to the complexity and diversity of photo album content. In addition, open-domain photo albums cover a broad range of topics ...