no code implementations • 5 Nov 2023 • Matteo Destro, Michael Gygli
We start from the insight that a good visual representation for periodic data should be sensitive to the phase of a cycle, but be invariant to the exact repetition, i. e. it should generate identical representations for a specific phase throughout all repetitions.
no code implementations • 24 Mar 2021 • Thomas Mensink, Jasper Uijlings, Alina Kuznetsova, Michael Gygli, Vittorio Ferrari
Our study leads to several insights and concrete recommendations: (1) for most tasks there exists a source which significantly outperforms ILSVRC'12 pre-training; (2) the image domain is the most important factor for achieving positive transfer; (3) the source dataset should \emph{include} the image domain of the target dataset to achieve best results; (4) at the same time, we observe only small negative effects when the image domain of the source task is much broader than that of the target; (5) transfer across task types can be beneficial, but its success is heavily dependent on both the source and target task types.
no code implementations • 8 Apr 2020 • Michael Gygli, Jasper Uijlings, Vittorio Ferrari
This paper proposes to make a first step towards compatible and hence reusable network components.
no code implementations • ECCV 2020 • Theodora Kontogianni, Michael Gygli, Jasper Uijlings, Vittorio Ferrari
Our approach enables the adaptation to a particular object and its background, to distributions shifts in a test set, to specific object classes, and even to large domain changes, where the imaging modality changes between training and testing.
Ranked #1 on Interactive Segmentation on DRIONS-DB
no code implementations • 4 Jun 2019 • Jordi Pont-Tuset, Michael Gygli, Vittorio Ferrari
This vocabulary represents the natural distribution of objects well and is learned directly from data, instead of being an educated guess done before collecting any labels.
no code implementations • 25 May 2019 • Michael Gygli, Vittorio Ferrari
We then combine the two stages: annotators draw an object bounding box via the mouse and simultaneously provide its class label via speech.
no code implementations • CVPR 2019 • Michael Gygli, Vittorio Ferrari
Modern approaches rely on a hierarchical organization of the vocabulary to reduce annotation time, but remain expensive (several minutes per image for the 200 classes in ILSVRC).
1 code implementation • 18 Apr 2018 • Ana García del Molino, Michael Gygli
Highlight detection models are typically trained to identify cues that make visual content appealing or interesting for the general public, with the objective of reducing a video to such moments.
no code implementations • 31 Dec 2017 • Arnaud Benard, Michael Gygli
The backbone of our system is a method for one-shot video object segmentation.
5 code implementations • 23 May 2017 • Michael Gygli
Shot boundary detection (SBD) is an important component of many video analysis tasks, such as action recognition, video indexing, summarization and editing.
1 code implementation • 1 May 2017 • Arun Balajee Vasudevan, Michael Gygli, Anna Volokitin, Luc van Gool
Although the problem of automatic video summarization has recently received a lot of attention, the problem of creating a video summary that also highlights elements relevant to a search query has been less studied.
1 code implementation • ICML 2017 • Michael Gygli, Mohammad Norouzi, Anelia Angelova
We approach structured output prediction by optimizing a deep value network (DVN) to precisely estimate the task loss on different output configurations for a given input.
no code implementations • ICCV 2017 • Santiago Manen, Michael Gygli, Dengxin Dai, Luc van Gool
We further validate our approach by crowdsourcing the PathTrack dataset, with more than 15, 000 person trajectories in 720 sequences.
1 code implementation • 3 Jan 2017 • Naoya Takahashi, Michael Gygli, Luc van Gool
Instead, combining visual features with our AENet features, which can be computed efficiently on a GPU, leads to significant performance improvements on action recognition and video highlight detection.
no code implementations • CVPR 2016 • Anna Volokitin, Michael Gygli, Xavier Boix
Many computational models of visual attention use image features and machine learning techniques to predict eye fixation locations as saliency maps.
1 code implementation • CVPR 2016 • Michael Gygli, Yale Song, Liangliang Cao
We introduce the novel problem of automatically generating animated GIFs from video.
no code implementations • 25 Apr 2016 • Naoya Takahashi, Michael Gygli, Beat Pfister, Luc van Gool
We propose a novel method for Acoustic Event Detection (AED).
Sound Multimedia
no code implementations • CVPR 2015 • Michael Gygli, Helmut Grabner, Luc van Gool
We present a novel method for summarizing raw, casually captured videos.
no code implementations • ECCV 2014 • Michael Gygli, Helmut Grabner, Hayko Riemenschneider, Luc van Gool
Thereby we focus on user videos, which are raw videos containing a set of interesting events.
no code implementations • CVPR 2013 • Xavier Boix, Michael Gygli, Gemma Roig, Luc van Gool
We demonstrate the capabilities of our formulation for both keypoint matching and image classification.