no code implementations • 27 Oct 2020 • Mert Kilickaya, Arnold W. M. Smeulders
The structure is in the form of a 2D composition that encodes the position and the category of the objects.
no code implementations • 30 Jun 2020 • Deepak K. Gupta, Efstratios Gavves, Arnold W. M. Smeulders
Specifically, we present structured dropout to mimick the change in latent codes under occlusion.
no code implementations • 18 Mar 2020 • Noureldien Hussein, Efstratios Gavves, Arnold W. M. Smeulders
We study the three properties of PIC and demonstrate its effectiveness in recognizing the long-range activities of Charades, Breakfast, and MultiThumos.
no code implementations • CVPR 2020 • Tom F. H. Runia, Kirill Gavrilyuk, Cees G. M. Snoek, Arnold W. M. Smeulders
For many of the physical phenomena around us, we have developed sophisticated models explaining their behavior.
no code implementations • 17 Oct 2019 • Tom F. H. Runia, Kirill Gavrilyuk, Cees G. M. Snoek, Arnold W. M. Smeulders
Nevertheless, inferring specifics from visual observations is challenging due to the high number of causally underlying physical parameters -- including material properties and external forces.
no code implementations • 5 Aug 2019 • Efstratios Gavves, Ran Tao, Deepak K. Gupta, Arnold W. M. Smeulders
Updating the tracker model with adverse bounding box predictions adds an unavoidable bias term to the learning.
no code implementations • 13 May 2019 • Noureldien Hussein, Efstratios Gavves, Arnold W. M. Smeulders
To represent them, related works opt for statistical pooling, which neglects the temporal structure.
Ranked #6 on Long-video Activity Recognition on Breakfast
no code implementations • 2 Apr 2019 • William Thong, Cees G. M. Snoek, Arnold W. M. Smeulders
These relationships enable them to cooperate for their mutual benefits for image retrieval.
3 code implementations • CVPR 2019 • Noureldien Hussein, Efstratios Gavves, Arnold W. M. Smeulders
This paper focuses on the temporal aspect for recognizing human activities in videos; an important visual cue that has long been undervalued.
Ranked #6 on Video Classification on Breakfast
no code implementations • 3 Sep 2018 • Berkay Kicanaoglu, Ran Tao, Arnold W. M. Smeulders
The generated orbit in the latent space records all the differences in pose in the original observational space, and as a result, the method is capable of finding subtle differences in pose.
1 code implementation • 18 Jun 2018 • Tom F. H. Runia, Cees G. M. Snoek, Arnold W. M. Smeulders
Estimating visual repetition from realistic video is challenging as periodic motion is rarely perfectly static and stationary.
no code implementations • 19 Mar 2018 • Silvia L. Pintea, Jan C. van Gemert, Arnold W. M. Smeulders
This enables each center to adjust the kernel space in its vicinity in correspondence with the topology of the targets --- a multi-modal approach.
1 code implementation • 19 Mar 2018 • Silvia L. Pintea, Jan C. van Gemert, Arnold W. M. Smeulders
This paper proposes motion prediction in single still images by learning it from a set of videos.
no code implementations • 19 Mar 2018 • Silvia L. Pintea, Pascal S. Mettes, Jan C. van Gemert, Arnold W. M. Smeulders
This method introduces an efficient manner of learning action categories without the need of feature estimation.
no code implementations • CVPR 2018 • Tom F. H. Runia, Cees G. M. Snoek, Arnold W. M. Smeulders
We consider the problem of estimating repetition in video, such as performing push-ups, cutting a melon or playing violin.
1 code implementation • 28 Nov 2017 • Ran Tao, Efstratios Gavves, Arnold W. M. Smeulders
Long-term tracking requires extreme stability to the multitude of model updates and robustness to the disappearance and loss of the target as such will inevitably happen.
no code implementations • CVPR 2017 • Zhenyang Li, Ran Tao, Efstratios Gavves, Cees G. M. Snoek, Arnold W. M. Smeulders
This paper strives to track a target object in a video.
Ranked #17 on Referring Expression Segmentation on J-HMDB
no code implementations • 2 Jun 2017 • Jörn-Henrik Jacobsen, Bert de Brabandere, Arnold W. M. Smeulders
Filters in convolutional networks are typically parameterized in a pixel basis, that does not take prior knowledge about the visual world into account.
no code implementations • CVPR 2017 • Noureldien Hussein, Efstratios Gavves, Arnold W. M. Smeulders
Given a text describing a novel event, the goal is to rank related videos accordingly.
no code implementations • 12 Mar 2017 • Jörn-Henrik Jacobsen, Edouard Oyallon, Stéphane Mallat, Arnold W. M. Smeulders
Multiscale hierarchical convolutional networks are structured deep convolutional networks where layers are indexed by progressively higher dimensional attributes, which are learned from training data.
no code implementations • 12 Oct 2016 • Hendrik Heuer, Christof Monz, Arnold W. M. Smeulders
This paper explores new evaluation perspectives for image captioning and introduces a noun translation task that achieves comparative image caption generation performance by translating from a set of nouns to captions.
no code implementations • 6 Oct 2016 • Svetlana Kordumova, Jan C. van Gemert, Cees G. M. Snoek, Arnold W. M. Smeulders
Second, we propose translating the things syntax in linguistic abstract statements and study their descriptive effect to retrieve scenes.
no code implementations • 23 May 2016 • Ran Tao, Arnold W. M. Smeulders, Shih-Fu Chang
Searching among instances from the same category as the query, the category-specific attributes outperform existing approaches by a large margin on shoes and cars and perform on par with the state-of-the-art on buildings.
no code implementations • CVPR 2016 • Ran Tao, Efstratios Gavves, Arnold W. M. Smeulders
In this paper we present a tracker, which is radically different from state-of-the-art trackers: we apply no model updating, no occlusion detection, no combination of trackers, no geometric matching, and still deliver state-of-the-art tracking performance, as demonstrated on the popular online tracking benchmark (OTB) and six very challenging YouTube videos.
3 code implementations • CVPR 2016 • Jörn-Henrik Jacobsen, Jan van Gemert, Zhongyu Lou, Arnold W. M. Smeulders
We combine these ideas into structured receptive field networks, a model which has a fixed filter basis and yet retains the flexibility of CNNs.
no code implementations • CVPR 2015 • Ran Tao, Arnold W. M. Smeulders, Shih-Fu Chang
This paper aims for generic instance search from one example where the instance can be an arbitrary 3D object like shoes, not just near-planar and one-sided instances like buildings and logos.
no code implementations • CVPR 2014 • Koen E. A. van de Sande, Cees G. M. Snoek, Arnold W. M. Smeulders
Finally, by multiple codeword assignments, we achieve exact and approximate Fisher vectors with FLAIR.
no code implementations • CVPR 2014 • Ran Tao, Efstratios Gavves, Cees G. M. Snoek, Arnold W. M. Smeulders
This paper aims for generic instance search from a single example.