How to effectively exchange and aggregate the information of multiple modalities (eg RGB
image and depth map) is a big challenge in the RGB-D salient object detection community …

Recently, supervised cross-modal hashing has attracted much attention and achieved
promising performance. To learn hash functions and binary codes, most methods globally …

As an effective image clustering tool, low-rank representation (LRR) can capture the intrinsic
representation of the observed samples. However, firstly, the good representation does not …

Detecting events from massive social media data in social networks can facilitate browsing,
search, and monitoring of real-time events by corporations, governments, and users. The …

Supervised cross-modal hashing has gained a lot of attention recently. However, most
existing methods learn binary codes or hash functions in a batch-based scheme, which is …

Recently, graph based hashing has gained much attention due to its effectiveness in multi-
media retrieval. Although several graph embedding based works have been designed and …

Cross-modal hashing methods have attracted considerable attention. Most pioneer
approaches only preserve the neighborhood relationship by constructing the correlations …

Choosing appropriate scenarios is critical for autonomous vehicles (AVs) safety testing. Real-
world crash scenarios can be used as critical scenarios to test the safety performance of …

Given a training set of face photo-sketch pairs, face sketch synthesis targets at learning a
mapping from the photo domain to the sketch domain. Despite the exciting progresses made …

Most existing object detection algorithms are trained based upon a set of fully annotated
object regions or bounding boxes, which are typically labor-intensive. On the contrary …