Search Results for author: Cees G. M. Snoek

Found 73 papers, 24 papers with code

Multiset-Equivariant Set Prediction with Approximate Implicit Differentiation

no code implementations23 Nov 2021 Yan Zhang, David W. Zhang, Simon Lacoste-Julien, Gertjan J. Burghouts, Cees G. M. Snoek

Most set prediction models in deep learning use set-equivariant operations, but they actually operate on multisets.

Feature and Label Embedding Spaces Matter in Addressing Image Classifier Bias

1 code implementation27 Oct 2021 William Thong, Cees G. M. Snoek

This paper strives to address image classifier bias, with a focus on both feature and label embedding spaces.


Diagnosing Errors in Video Relation Detectors

1 code implementation25 Oct 2021 Shuo Chen, Pascal Mettes, Cees G. M. Snoek

Video relation detection forms a new and challenging problem in computer vision, where subjects and objects need to be localized spatio-temporally and a predicate label needs to be assigned if and only if there is an interaction between the two.

Action Localization Object Detection

Generative Kernel Continual Learning

no code implementations29 Sep 2021 Mohammad Mahdi Derakhshani, XianTong Zhen, Ling Shao, Cees G. M. Snoek

Kernel continual learning by Derakhshani et al. (2021) has recently emerged as a strong continual learner due to its non-parametric ability to tackle task interference and catastrophic forgetting.

Continual Learning

Social Fabric: Tubelet Compositions for Video Relation Detection

1 code implementation ICCV 2021 Shuo Chen, Zenglin Shi, Pascal Mettes, Cees G. M. Snoek

We also propose Social Fabric: an encoding that represents a pair of object tubelets as a composition of interaction primitives.

Skeleton-Contrastive 3D Action Representation Learning

1 code implementation8 Aug 2021 Fida Mohammad Thoker, Hazel Doughty, Cees G. M. Snoek

In particular, we propose inter-skeleton contrastive learning, which learns from multiple different input skeleton representations in a cross-contrastive manner.

Action Recognition Contrastive Learning +3

Feature-Supervised Action Modality Transfer

no code implementations6 Aug 2021 Fida Mohammad Thoker, Cees G. M. Snoek

This paper strives for action recognition and detection in video modalities like RGB, depth maps or 3D-skeleton sequences when only limited modality-specific labeled examples are available.

Action Recognition Optical Flow Estimation +1

Kernel Continual Learning

1 code implementation12 Jul 2021 Mohammad Mahdi Derakhshani, XianTong Zhen, Ling Shao, Cees G. M. Snoek

We further introduce variational random features to learn a data-driven kernel for each task.

Continual Learning Variational Inference

On Measuring and Controlling the Spectral Bias of the Deep Image Prior

no code implementations2 Jul 2021 Zenglin Shi, Pascal Mettes, Subhransu Maji, Cees G. M. Snoek

The experiments on denoising, inpainting and super-resolution show that our method no longer suffers from performance degradation during optimization, relieving us from the need for an oracle criterion to stop early.

Denoising Super-Resolution

Recurrently Predicting Hypergraphs

1 code implementation26 Jun 2021 David W. Zhang, Gertjan J. Burghouts, Cees G. M. Snoek

This work considers predicting the relational structure of a hypergraph for a given set of vertices, as common for applications in particle physics, biological systems and other complex combinatorial problems.

Meta-Learning with Variational Semantic Memory for Word Sense Disambiguation

no code implementations ACL 2021 Yingjun Du, Nithin Holla, XianTong Zhen, Cees G. M. Snoek, Ekaterina Shutova

A critical challenge faced by supervised word sense disambiguation (WSD) is the lack of large annotated datasets with sufficient coverage of words in their diversity of senses.

Meta-Learning Variational Inference +1

Unsharp Mask Guided Filtering

1 code implementation2 Jun 2021 Zenglin Shi, Yunlu Chen, Efstratios Gavves, Pascal Mettes, Cees G. M. Snoek

The state-of-the-art leverages deep networks to estimate the two core coefficients of the guided filter.


Attentional Prototype Inference for Few-Shot Semantic Segmentation

no code implementations14 May 2021 Haoliang Sun, Xiankai Lu, Haochen Wang, Yilong Yin, XianTong Zhen, Cees G. M. Snoek, Ling Shao

In this work, we propose attentional prototype inference (API), a probabilistic latent variable framework for few-shot semantic segmentation.

Bayesian Inference Few-Shot Semantic Segmentation +1

A Bit More Bayesian: Domain-Invariant Learning with Uncertainty

1 code implementation9 May 2021 Zehao Xiao, Jiayi Shen, XianTong Zhen, Ling Shao, Cees G. M. Snoek

Domain generalization is challenging due to the domain shift and the uncertainty caused by the inaccessibility of target domain data.

Bayesian Inference Domain Generalization

MetaKernel: Learning Variational Random Features with Limited Labels

no code implementations8 May 2021 Yingjun Du, Haoliang Sun, XianTong Zhen, Jun Xu, Yilong Yin, Ling Shao, Cees G. M. Snoek

Specifically, we propose learning variational random features in a data-driven manner to obtain task-specific kernels by leveraging the shared knowledge provided by related tasks in a meta-learning setting.

Few-Shot Image Classification Variational Inference

Motion-Augmented Self-Training for Video Recognition at Smaller Scale

no code implementations ICCV 2021 Kirill Gavrilyuk, Mihir Jain, Ilia Karmanov, Cees G. M. Snoek

With the motion model we generate pseudo-labels for a large unlabeled video collection, which enables us to transfer knowledge by learning to predict these pseudo-labels with an appearance model.

Action Recognition Optical Flow Estimation +2

Safe Fakes: Evaluating Face Anonymizers for Face Detectors

no code implementations23 Apr 2021 Sander R. Klomp, Matthew van Rijn, Rob G. J. Wijnhoven, Cees G. M. Snoek, Peter H. N. de With

Our experiments investigate the suitability of anonymization methods for maintaining face detector performance, the effect of detectors overtraining on anonymization artefacts, dataset size for training an anonymizer, and the effect of training time of anonymization GANs.

Face Detection

Object Priors for Classifying and Localizing Unseen Actions

1 code implementation10 Apr 2021 Pascal Mettes, William Thong, Cees G. M. Snoek

This work strives for the classification and localization of human actions in videos, without the need for any labeled video training examples.

Action Classification Action Localization +2

TubeR: Tube-Transformer for Action Detection

no code implementations2 Apr 2021 Jiaojiao Zhao, Xinyu Li, Chunhui Liu, Shuai Bing, Hao Chen, Cees G. M. Snoek, Joseph Tighe

In this paper, we propose TubeR: the first transformer based network for end-to-end action detection, with an encoder and decoder optimized for modeling action tubes with variable lengths and aspect ratios.

Action Detection Video Understanding

LiftPool: Bidirectional ConvNet Pooling

no code implementations ICLR 2021 Jiaojiao Zhao, Cees G. M. Snoek

Pooling is a critical operation in convolutional neural networks for increasing receptive fields and improving robustness to input variations.

Image Classification Image-to-Image Translation +2

Repetitive Activity Counting by Sight and Sound

1 code implementation CVPR 2021 Yunhua Zhang, Ling Shao, Cees G. M. Snoek

We also introduce a variant of this dataset for repetition counting under challenging vision conditions.

Variational Invariant Learning for Bayesian Domain Generalization

no code implementations1 Jan 2021 Zehao Xiao, Jiayi Shen, XianTong Zhen, Ling Shao, Cees G. M. Snoek

In the probabilistic modeling framework, we introduce a domain-invariant principle to explore invariance across domains in a unified way.

Domain Generalization

Learning to Learn Variational Semantic Memory

no code implementations NeurIPS 2020 XianTong Zhen, Yingjun Du, Huan Xiong, Qiang Qiu, Cees G. M. Snoek, Ling Shao

The variational semantic memory accrues and stores semantic information for the probabilistic inference of class prototypes in a hierarchical Bayesian framework.

Few-Shot Learning Variational Inference

Bias-Awareness for Zero-Shot Learning the Seen and Unseen

1 code implementation25 Aug 2020 William Thong, Cees G. M. Snoek

We propose a bias-aware learner to map inputs to a semantic embedding space for generalized zero-shot learning.

Generalized Zero-Shot Learning

Localizing the Common Action Among a Few Videos

1 code implementation ECCV 2020 Pengwan Yang, Vincent Tao Hu, Pascal Mettes, Cees G. M. Snoek

The start and end of an action in a long untrimmed video is determined based on just a hand-full of trimmed video examples containing the same action, without knowing their common class label.

Action Localization

Open Cross-Domain Visual Search

2 code implementations19 Nov 2019 William Thong, Pascal Mettes, Cees G. M. Snoek

In this paper, we make the step towards an open setting where multiple visual domains are available.

Domain Adaptation

Go with the Flow: Perception-refined Physics Simulation

no code implementations17 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.

Spherical Regression: Learning Viewpoints, Surface Normals and 3D Rotations on n-Spheres

2 code implementations CVPR 2019 Shuai Liao, Efstratios Gavves, Cees G. M. Snoek

We observe many continuous output problems in computer vision are naturally contained in closed geometrical manifolds, like the Euler angles in viewpoint estimation or the normals in surface normal estimation.

3D Rotation Estimation Surface Normals Estimation +1

Dance with Flow: Two-in-One Stream Action Detection

1 code implementation CVPR 2019 Jiaojiao Zhao, Cees G. M. Snoek

With only half the computation and parameters of the state-of-the-art two-stream methods, our two-in-one stream still achieves impressive results on UCF101-24, UCFSports and J-HMDB.

Ranked #2 on Action Detection on UCF101-24 (mAP metric)

Action Detection Optical Flow Estimation

Counting with Focus for Free

1 code implementation ICCV 2019 Zenglin Shi, Pascal Mettes, Cees G. M. Snoek

To assist both the density estimation and the focus from segmentation, we also introduce an improved kernel size estimator for the point annotations.

Density Estimation

Anomaly Locality in Video Surveillance

no code implementations29 Jan 2019 Federico Landi, Cees G. M. Snoek, Rita Cucchiara

This paper strives for the detection of real-world anomalies such as burglaries and assaults in surveillance videos.

Anomaly Detection

Hyperspherical Prototype Networks

2 code implementations NeurIPS 2019 Pascal Mettes, Elise van der Pol, Cees G. M. Snoek

This paper introduces hyperspherical prototype networks, which unify classification and regression with prototypes on hyperspherical output spaces.

Classification General Classification

Pixelated Semantic Colorization

no code implementations27 Jan 2019 Jiaojiao Zhao, Jungong Han, Ling Shao, Cees G. M. Snoek

We propose two ways to incorporate object semantics into the colorization model: through a pixelated semantic embedding and a pixelated semantic generator.

Colorization Semantic Segmentation

Pixel-level Semantics Guided Image Colorization

no code implementations5 Aug 2018 Jiaojiao Zhao, Li Liu, Cees G. M. Snoek, Jungong Han, Ling Shao

While many image colorization algorithms have recently shown the capability of producing plausible color versions from gray-scale photographs, they still suffer from the problems of context confusion and edge color bleeding.

Colorization Semantic Segmentation

Video Time: Properties, Encoders and Evaluation

no code implementations18 Jul 2018 Amir Ghodrati, Efstratios Gavves, Cees G. M. Snoek

Time-aware encoding of frame sequences in a video is a fundamental problem in video understanding.

Video Understanding

Spatio-Temporal Instance Learning: Action Tubes from Class Supervision

no code implementations8 Jul 2018 Pascal Mettes, Cees G. M. Snoek

Rather than disconnecting the spatio-temporal learning from the training, we propose Spatio-Temporal Instance Learning, which enables action localization directly from box proposals in video frames.

Multiple Instance Learning Spatio-Temporal Action Localization +1

Repetition Estimation

1 code implementation18 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.

Pointly-Supervised Action Localization

no code implementations29 May 2018 Pascal Mettes, Cees G. M. Snoek

Experimental evaluation on three action localization datasets shows our pointly-supervised approach (i) is as effective as traditional box-supervision at a fraction of the annotation cost, (ii) is robust to sparse and noisy point annotations, (iii) benefits from pseudo-points during inference, and (iv) outperforms recent weakly-supervised alternatives.

Action Localization Multiple Instance Learning +1

Real-World Repetition Estimation by Div, Grad and Curl

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.

The New Modality: Emoji Challenges in Prediction, Anticipation, and Retrieval

no code implementations30 Jan 2018 Spencer Cappallo, Stacey Svetlichnaya, Pierre Garrigues, Thomas Mensink, Cees G. M. Snoek

Over the past decade, emoji have emerged as a new and widespread form of digital communication, spanning diverse social networks and spoken languages.

Predicting Visual Features from Text for Image and Video Caption Retrieval

1 code implementation5 Sep 2017 Jianfeng Dong, Xirong Li, Cees G. M. Snoek

This paper strives to find amidst a set of sentences the one best describing the content of a given image or video.

Video Description

Localizing Actions from Video Labels and Pseudo-Annotations

no code implementations28 Jul 2017 Pascal Mettes, Cees G. M. Snoek, Shih-Fu Chang

The goal of this paper is to determine the spatio-temporal location of actions in video.

Action Localization

Searching Scenes by Abstracting Things

no code implementations6 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.

Tubelets: Unsupervised action proposals from spatiotemporal super-voxels

no code implementations7 Jul 2016 Mihir Jain, Jan van Gemert, Hervé Jégou, Patrick Bouthemy, Cees G. M. Snoek

First, inspired by selective search for object proposals, we introduce an approach to generate action proposals from spatiotemporal super-voxels in an unsupervised manner, we call them Tubelets.

Action Localization

Spot On: Action Localization from Pointly-Supervised Proposals

no code implementations26 Apr 2016 Pascal Mettes, Jan C. van Gemert, Cees G. M. Snoek

Rather than annotating boxes, we propose to annotate actions in video with points on a sparse subset of frames only.

Action Localization Multiple Instance Learning +1

Word2VisualVec: Image and Video to Sentence Matching by Visual Feature Prediction

no code implementations23 Apr 2016 Jianfeng Dong, Xirong Li, Cees G. M. Snoek

This paper strives to find the sentence best describing the content of an image or video.

The ImageNet Shuffle: Reorganized Pre-training for Video Event Detection

no code implementations23 Feb 2016 Pascal Mettes, Dennis C. Koelma, Cees G. M. Snoek

To deal with the problems of over-specific classes and classes with few images, we introduce a bottom-up and top-down approach for reorganization of the ImageNet hierarchy based on all its 21, 814 classes and more than 14 million images.

Event Detection Object Recognition

VideoStory Embeddings Recognize Events when Examples are Scarce

no code implementations8 Nov 2015 Amirhossein Habibian, Thomas Mensink, Cees G. M. Snoek

In our proposed embedding, which we call VideoStory, the correlations between the terms are utilized to learn a more effective representation by optimizing a joint objective balancing descriptiveness and predictability. We show how learning the VideoStory using a multimodal predictability loss, including appearance, motion and audio features, results in a better predictable representation.

Event Detection

TagBook: A Semantic Video Representation without Supervision for Event Detection

no code implementations10 Oct 2015 Masoud Mazloom, Xirong Li, Cees G. M. Snoek

We consider the problem of event detection in video for scenarios where only few, or even zero examples are available for training.

Event Detection Image Retrieval

Active Transfer Learning with Zero-Shot Priors: Reusing Past Datasets for Future Tasks

no code implementations ICCV 2015 Efstratios Gavves, Thomas Mensink, Tatiana Tommasi, Cees G. M. Snoek, Tinne Tuytelaars

How can we reuse existing knowledge, in the form of available datasets, when solving a new and apparently unrelated target task from a set of unlabeled data?

Active Learning General Classification +2

Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval

1 code implementation28 Mar 2015 Xirong Li, Tiberio Uricchio, Lamberto Ballan, Marco Bertini, Cees G. M. Snoek, Alberto del Bimbo

Where previous reviews on content-based image retrieval emphasize on what can be seen in an image to bridge the semantic gap, this survey considers what people tag about an image.

Content-Based Image Retrieval

Action Localization with Tubelets from Motion

no code implementations CVPR 2014 Mihir Jain, Jan van Gemert, Herve Jegou, Patrick Bouthemy, Cees G. M. Snoek

Our approach significantly outperforms the state-of-the-art on both datasets, while restricting the search of actions to a fraction of possible bounding box sequences.

Action Localization

Fisher and VLAD with FLAIR

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.

COSTA: Co-Occurrence Statistics for Zero-Shot Classification

no code implementations CVPR 2014 Thomas Mensink, Efstratios Gavves, Cees G. M. Snoek

In this paper we aim for zero-shot classification, that is visual recognition of an unseen class by using knowledge transfer from known classes.

Classification Few-Shot Learning +3

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