In this paper we present a novel approach to improve temporal matching kernel (TMK) for
video retrieval tasks. TMK has the ability to align videos during retrieval, but provides little to …

In this paper we introduce ViSiL, a Video Similarity Learning architecture that considers fine-
grained Spatio-Temporal relations between pairs of videos--such relations are typically lost …

With the explosive growth of web videos in recent years, large-scale Content-Based Video
Retrieval (CBVR) becomes increasingly essential in video filtering, recommendation, and …

The performance of text-video retrieval has been significantly improved by vision-language
cross-modal learning schemes. The typical solution is to directly align the global video-level …

This paper presents a method that embeds stability-sensitive filter in the temporal matching
kernel with explicit feature maps (TE). The added filter improves the robustness of TE to …

The current research focus on Content-Based Video Retrieval requires higher-level video
representation describing the long-range semantic dependencies of relevant incidents …

In recent times, exploration of multimedia required ever increasing demand and application
for intelligent video retrieval from repositories. This paper presents an efficient video retrieval …

We propose a video retrieval system for everyday scenes with common objects. Our system
exploits the predictions made by deep neural networks for image understanding tasks using …

An efficient video retrieval system is essential to search relevant video contents from a large
set of video clips, which typically contain several heterogeneous video clips to match with. In …

This paper considers a learnable approach for comparing and aligning videos. Our
architecture builds upon and revisits temporal match kernels within neural networks: we …