output
20022025
most citedEmpirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

10.8k citations

Showing 2021 · cs.CVShow all

8 papers · 2 filters

cs.CV2021

PolyTrack: Tracking with Bounding Polygons

Gaspar Faure, Hughes Perreault, Guillaume-Alexandre Bilodeau +1

In this paper, we present a novel method called PolyTrack for fast multi-object tracking and segmentation using bounding polygons. Polytrack detects objects by producing heatmaps o…

cs.CV20215 cited

Simple Video Generation using Neural ODEs

David Kanaa, Vikram Voleti, Samira Ebrahimi Kahou +1

Despite having been studied to a great extent, the task of conditional generation of sequences of frames, or videos, remains extremely challenging. It is a common belief that a key…

cs.CV2021

MeNToS: Tracklets Association with a Space-Time Memory Network

Mehdi Miah, Guillaume-Alexandre Bilodeau, Nicolas Saunier

We propose a method for multi-object tracking and segmentation (MOTS) that does not require fine-tuning or per benchmark hyperparameter selection. The proposed method addresses par…

cs.CV20211 cited

Multiple Convolutional Features in Siamese Networks for Object Tracking

Zhenxi Li, Guillaume-Alexandre Bilodeau, Wassim Bouachir

Siamese trackers demonstrated high performance in object tracking due to their balance between accuracy and speed. Unlike classification-based CNNs, deep similarity networks are sp…

cs.CV2021

MFST: Multi-Features Siamese Tracker

Zhenxi Li, Guillaume-Alexandre Bilodeau, Wassim Bouachir

Siamese trackers have recently achieved interesting results due to their balance between accuracy and speed. This success is mainly due to the fact that deep similarity networks we…

cs.CV202117 cited

Adaptable Deformable Convolutions for Semantic Segmentation of Fisheye Images in Autonomous Driving Systems

Clément Playout, Ola Ahmad, Freddy Lecue +1

Advanced Driver-Assistance Systems rely heavily on perception tasks such as semantic segmentation where images are captured from large field of view (FoV) cameras. State-of-the-art…