Search Results for author: Michael Gygli

Found 20 papers, 6 papers with code

CycleCL: Self-supervised Learning for Periodic Videos

no code implementations5 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.

Contrastive Learning Self-Supervised Learning

Factors of Influence for Transfer Learning across Diverse Appearance Domains and Task Types

no code implementations24 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.

Autonomous Driving Depth Estimation +6

Continuous Adaptation for Interactive Object Segmentation by Learning from Corrections

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.

Interactive Segmentation Object +1

Natural Vocabulary Emerges from Free-Form Annotations

no code implementations4 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.

Efficient Object Annotation via Speaking and Pointing

no code implementations25 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.


Fast Object Class Labelling 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).


PHD-GIFs: Personalized Highlight Detection for Automatic GIF Creation

1 code implementation18 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.

Highlight Detection

Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural Networks

5 code implementations23 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.

Action Recognition Boundary Detection +2

Query-adaptive Video Summarization via Quality-aware Relevance Estimation

1 code implementation1 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.

Video Summarization

Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs

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.

General Classification Image Segmentation +3

PathTrack: Fast Trajectory Annotation with Path Supervision

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.

Multiple Object Tracking Object +1

AENet: Learning Deep Audio Features for Video Analysis

1 code implementation3 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.

Action Recognition Data Augmentation +4

Predicting When Saliency Maps Are Accurate and Eye Fixations Consistent

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.

Object Object Recognition

Video2GIF: Automatic Generation of Animated GIFs from Video

1 code implementation CVPR 2016 Michael Gygli, Yale Song, Liangliang Cao

We introduce the novel problem of automatically generating animated GIFs from video.

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