Italy


Most deep learning-based salient object detection (SOD) methods tried to manipulate the convolution block to effectively capture the context of object. In this paper, we propose a novel method, called progressive and multi-task learning scheme, to extract the context of object by only manipulating the learning scheme without changing the network architecture. The progressive learning scheme is a method to grow the decoder progressively in the train phase. In other words, starting from easier low-resolution layers, it gradually adds high-resolution layers. Although the progressive learning successfully captures the context of object, its output boundary tends to be rough. To solve this problem, we also propose a multi-task learning (MTL) scheme that processes the object saliency map and contour in a
single network jointly. The proposed MTL scheme trains the network in an edge-preserved direction through an auxiliary branch that learns contours. The proposed a learning scheme can be combined with other convolution block manipulation methods. Extensive experiments on five datasets show that the proposed method performs best compared with state-of-the-art methods in most cases.

ICPR 2020

Coarse to Fine: Progressive and Multi-Task Learning for Salient Object Detection

progressive learning

salient object detection

multi-task learning

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


poster


We present our work on video frame interpolation that directly generates the middle frame without an intermediate step such as motion or kernel estimation. Although simple, our method is able to compete against state-of-the-art methods on several datasets.

Residual Learning of Video Frame Interpolation Using Convolutional LSTM

In this paper, we propose a camera calibration method for surveillance cameras that uses the image projection of parallel 3D line segments of the same length. We assume that vertical line segments are perpendicular to the ground plane and their bottom end points are on the ground plane. Under this assumption, the camera parameters can be directly determined from at least two line segments without estimating vanishing points. By extending the minimal solution, we devise a closed-form solution to the least-squares case with more than two line segments. Lens distortion is jointly optimized in bundle adjustment. Evaluation of synthetic data showed that the optimal depression angle of a camera is around 50 degrees. In real data evaluation, we used the joints of pedestrians as vertical line segments. The experimental results on public datasets showed that the proposed method used with a human pose detector can accurately calibrate wide-angle cameras that have radial distortion.

Camera Calibration Using Parallel Line Segments

This paper addresses the challenging problem of generating videos with human action semantics. Unlike previous work which predict future frames in a single forward pass, this paper introduces the cycle constraints in both forward and backward passes in the generation of human actions. This is achieved by enforcing the appearance and motion consistency across a sequence of frames generated in the future. The approach consists of two stages. In the first stage, the pose of a human body is generated. In the second stage, an image generator is used to generate future frames by using (a) generated human poses in the future from the first stage, (b) the single observed human pose, and (c) the single corresponding future frame. The experiments are performed on three datasets: Weizmann dataset
involving simple human actions, Penn Action dataset and UCF-101 dataset containing complicated human actions, especially in sports. The results from these experiments demonstrate the effectiveness of the proposed approach.

The Role of Cycle Consistency for Generating Better Human Action Videos from a Single Frame


Within the research of scene text detection, some previous work has already achieved significant accuracy and efficiency. However, most of the work was generally done without considering about the implicit relationship between detection and eye movements. In this paper, we propose a new method for scene
text detection especially for its refinement based on reinforcement learning. The idea of this method is inspired by Saccadic Eye Movements and Peripheral Vision. A saccade makes it possible for humans to orient the gaze to the location where a visual object has appeared. Peripheral vision gathers visual information of surroundings which provides supplement to foveal vision during gazing. We propose a simple pipeline, imitating the way human eyes do a saccade and collect peripheral information, to locate scene text roughly and to refine multi-scale vision field iteratively using reinforcement learning.

RLST: A Reinforcement Learning Approach to Scene Text Detection Refinement


This paper presents a novel deep learning framework for the electrocardiogram (ECG) heartbeat classification. Although there have been some studies with excellent overall accuracy, these studies have not been very accurate in the diagnosis of arrhythmia classes especially such as supraventricular ectopic beat (SVEB) and ventricular ectopic beat (VEB). In our work, we propose a Multi-Scale and Attention based ResNet for heartbeat classification in intra-patient and inter-patient paradigms respectively. Firstly, we extract shallow features from a convolutional layer. Secondly, the shallow features are sent into three branches with different convolution kernels in order to combine receptive fields of different sizes. Finally, fully connected layers are used to classify the heartbeat. Besides, we design
a new attention mechanism based on the characteristics of heartbeat data. At last, extensive experiments on benchmark dataset demonstrate the effectiveness of our proposed model.

Multi-Scale and Attention based ResNet for Heartbeat Classification

Convolutional neural networks (CNNs) are being used in various fields related to image recognition and are achieving high recognition accuracy. However, most existing CNNs do not consider uncertainty in their predictions; that is, they do not account for the difficulty of prediction, and the extent to which their predictions are reliable is unclear. This problem is considered to be the cause of erroneous decisions when we use CNNs in practice. By considering the uncertainty of the prediction result, it is thought that recognition accuracy would improve, and erroneous decisions would be suppressed. We propose a Bayesian attention branch network (Bayesian ABN) that incorporates uncertainty into an attention branch network (ABN). The method incorporates a Bayesian neural network (Bayesian NN) into the ABN to account for uncertainty in the prediction result. Also, it outputs prediction results from two branches and chooses the one having the lower uncertainty. In evaluations using standard object recognition datasets, we confirmed that the proposed method improves the accuracy and reliability of CNNs.

Improving reliability of attention branch network by introducing uncertainty

Both two-stream ConvNet and 3D ConvNet are widely used in action recognition. However, both methods are not efficient for deployment: calculating optical flow is very slow, while 3D convolution is computationally expensive. Our key insight is that the motion information from optical flow maps is complementary to the motion information from 3D ConvNet. Instead of simply combining these two methods, we propose two novel techniques to enhance the performance with less computational cost: fixed-motion-accumulation and balanced-motion-policy. With these two techniques, we propose a novel framework called Efficient Motion Complementary Network(EMC-Net) that enjoys both high efficiency and high performance. We conduct extensive experiments on Kinetics, UCF101, and Jester datasets. We achieve notably higher performance while consuming 4.7 times less computation than I3D, 11.6 times less computation than ECO, 17.8 times less computation than R(2+1)D. On Kinetics dataset, we achieve 2.6% better performance than the recent proposed TSM with 1.4 times fewer FLOPs and 10ms faster on K80 GPU.

Motion Complementary Network for Efficient Action Recognition


Hashing has been widely studied for cross-modal retrieval due to its promising efficiency and effectiveness in massive data analysis. However, most existing supervised hashing has the limitations of inefficiency for very large-scale search and intractable discrete constraint for hash codes learning. In this paper, we propose a new supervised hashing method, namely, Discrete Semantic Matrix Factorization Hashing (DSMFH), for cross-modal retrieval. First, we conduct the matrix factorization via directly utilizing the available label information to obtain a latent representation, so that both the inter-modality and intra-modality similarities are well preserved. Then, we simultaneously learn the discriminative hash codes and corresponding hash functions by deriving the matrix factorization into a discrete optimization. Finally, we adopt an alternatively iterative procedure to efficiently optimize the matrix factorization and discrete learning. Extensive experimental results on three widely used image-tag databases demonstrate the superiority of the DSMFH over state-of-the-art cross-modal hashing methods.

Discrete Semantic Matrix Factorization Hashing for Cross-Modal Retrieval

Subspace clustering is a useful technique for many computer vision applications in which the intrinsic dimension of high-dimensional data is often smaller than the ambient dimension. Spectral clustering, as one of the main approaches to subspace clustering, often takes on a sparse representation or a low-rank representation to learn a block diagonal self-representation matrix for subspace generation. However, existing methods require solving a large scale convex optimization problem with a large set of data, with computational complexity reaches O(N^3) for N data points. Therefore, the efficiency and scalability of traditional spectral clustering methods can not be guaranteed for large scale datasets. In this paper, we propose a subspace clustering model based on the Kronecker product. Due to the property that the Kronecker product of a block diagonal matrix with any other matrix is still a block diagonal matrix, we can efficiently learn the representation matrix which is formed by the Kronecker product of k smaller matrices. By doing so, our model significantly reduces the computational complexity to O(kN^(3/k)). Furthermore, our model is general in nature and can be adapted to different regularization based subspace clustering methods. Experimental results on two public datasets show that our model significantly improves the efficiency compared with several state-of-the-art methods. Moreover, we have conducted experiments on synthetic data to verify the scalability of our model for large scale datasets.

Fast Subspace Clustering Based on the Kronecker Product

Deep neural networks (DNN) have been applied recently to different domains and
perform better than classical state-of-the-art methods. However the high level of 
performances of DNNs is most often obtained with networks 
containing millions of parameters and for which training requires 
substantial computational power. 
To deal with this computational issue proximal regularization methods have been proposed in the literature but they are time consuming.\\
In this paper, we propose instead a constrained approach. 
We studied algorithms for different constraints: the classical $\ell_1$ unstructured constraint and structured constraints such as the $\ell_{2,1} $ constraint (Group LASSO). We propose a new $\ell_{1,1} $ structured constraint for which we provide a new projection algorithm. 
Finally, we used the recent "Lottery optimizer" replacing the threshold by our $\ell_{1,1} $ projection.
We demonstrate the effectiveness of this method with three popular datasets (MNIST, Fashion MNIST and CIFAR).
Experiments with these datasets show that our projection method using this new $\ell_{1,1} $ structured constraint provides the best decrease in memory and computational power.

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Coarse to Fine: Progressive and Multi-Task Learning for Salient Object Detection

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