5 papers
MovingFashion: a Benchmark for the Video-to-Shop Challenge
Marco Godi, Christian Joppi, Geri Skenderi +1
Retrieving clothes which are worn in social media videos (Instagram, TikTok) is the latest frontier of e-fashion, referred to as "video-to-shop" in the computer vision literature.…
Texel-Att: Representing and Classifying Element-based Textures by Attributes
Marco Godi, Christian Joppi, Andrea Giachetti +2
Element-based textures are a kind of texture formed by nameable elements, the texels [1], distributed according to specific statistical distributions; it is of primary importance i…
Texture Retrieval in the Wild through detection-based attributes
Christian Joppi, Marco Godi, Andrea Giachetti +2
Capturing the essence of a textile image in a robust way is important to retrieve it in a large repository, especially if it has been acquired in the wild (by taking a photo of the…
SIMCO: SIMilarity-based object COunting
Marco Godi, Christian Joppi, Andrea Giachetti +1
We present SIMCO, the first agnostic multi-class object counting approach. SIMCO starts by detecting foreground objects through a novel Mask RCNN-based architecture trained beforeh…
Indirect Match Highlights Detection with Deep Convolutional Neural Networks
Marco Godi, Paolo Rota, Francesco Setti
Highlights in a sport video are usually referred as actions that stimulate excitement or attract attention of the audience. A big effort is spent in designing techniques which find…