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Works190 from public data
- Sequential Deep Learning for Human Action Recognition817
A fully automated deep model, which learns to classify human actions without using any prior knowledge is proposed, which outperforms existing deep models, and gives comparable results with the best related works.
- The 2005 PASCAL Visual Object Classes Challenge742
This chapter provides details of the datasets, algorithms used by the teams, evaluation criteria, and results achieved in the PASCAL Visual Object Classes Challenge.
- Face detection using quantized skin color regions merging and wavelet packet analysis670
An efficient and reliable probabilistic metric derived from the Bhattacharrya distance is used in order to classify the extracted feature vectors into face or nonface areas, using some prototype face area vectors, acquired in a previous training stage.
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- The Visual Object Tracking VOT2014 Challenge Results588
The Visual Object Tracking challenge 2014, VOT2014, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance and introduces a new dataset with full annotation of targets by rotated bounding boxes and per-frame attribute.
- Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment210
A Bayesian approach to Predictive Uncertainty and a Lexical Alignment Model for Probabilistic Textual Entailment are presented.
- Wavelet packet analysis for face recognition161
It is shown how an efficient and reliable probabilistic metric derived from the Bhattacharrya distance can be used in order to classify the face feature vectors into person classes.
- Simplifying ConvNets for Fast Learning144
This paper proposes different strategies for simplifying filters, used as feature extractors, to be learnt in convolutional neural networks (ConvNets) in order to modify the hypothesis space, and to speed-up learning and processing times.
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- Action Classification in Soccer Videos with Long Short-Term Memory Recurrent Neural Networks130
Experimental results show that the proposed approach for action classification in soccer videos outperforms classification methods of related works, and that the combination of the two features (BoW and dominant motion) leads to a classification rate of 92%.
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- Spatio-Temporal Convolutional Sparse Auto-Encoder for Sequence Classification84
A novel learning-based approach for video sequence classification that automatically learns a sparse shift-invariant representation of the local 2D+t salient information, without any use of prior knowledge is presented.
- Evaluation of video activity localizations integrating quality and quantity measurements83
A new performance metric addressing and unifying the qualitative and quantitative aspects of the performance measures is proposed, which has been tested on several activity recognition algorithms participating in the ICPR 2012 HARL competition.
- BLSTM-RNN Based 3D Gesture Classification80
A new robust method for inertial MEM (MicroElectroMechanical systems) 3D gesture recognition based on Bidirectional Long Short-Term Memory Recurrent Neural Networks (BLSTM-RNN) for gesture classification from raw MEM data is presented.
- TEXT DETECTION WITH CONVOLUTIONAL NEURAL NETWORKS80
This work presents an approach based on convolutional neural networks to detect and localize horizontal text lines from raw color pixels and demonstrates that it can outperform other methods on the real-world test set of ICDAR’03.
- Deep Model Compression and Architecture Optimization for Embedded Systems: A Survey79
A survey of methods suitable for porting deep neural networks on resource-limited devices, especially for smart cameras, and introduces the methods to enhance networks structures as well as neural architecture search techniques.
- A Wavelet-based Framework for Face Recognition75
A new scheme for face recognition using a wavelet packet decomposition is presented, and an efficient and reliable probalistic metric derived from the Bhattacharrya distance is used in order to classify the face feature vectors into person classes.
- Bayesian Level Sets for Image Segmentation73
A level set formulation is used to model the boundaries of the image regions and a new Multilabel Fast Marching is introduced for the evolution of the region contours toward the segmentation result, leading to the Bayesian Level Set methodology.
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- Pose Estimation using Point and Line Correspondences68
This paper presents two fast methods for estimating the 3D pose using 2D to 3D point and line correspondences and concludes that the iterative paraperspective method has better convergence properties than the iteratively improved weak perspective method.
- Siamese multi-layer perceptrons for dimensionality reduction and face identification59
While the classical MLP fixes the dimension of the output space, the siamese MLP allows flexible output dimension, hence it is applied for visualization of the dimensionality reduction to the 2-d and 3-d spaces.
- Contribution of recurrent connectionist language models in improving LSTM-based Arabic text recognition in videos58
This work introduces recurrent connectionist language modeling in order to improve Long-Short Term Memory (LSTM) based Arabic text recognition in videos, and proposes a modified beam search algorithm that uses both OCR and language model probabilities in parallel at each decoding time-step.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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