Italy

Self-attention and encoder-decoder have been widely used in the deep neural network for monocular depth estimation. The self-attention mechanism is capable of capturing long-range dependencies, while the encoder-decoder structure can capture detailed structural information by gradually recovering spatial information. In this work, we combine the advantages of both methods. Specifically, our proposed model, DGNet, extends EMANet by adding an effective decoder module to progressively refine the coarse depth map. In the decoder stage, we design a dynamic guided upsampling module that employs a dynamically generated kernel conditioned on low-level features to guide the upsampling of the coarse depth map.

ICPR 2020

Dynamic Guided Network for Monocular Depth Estimation

dynamic guidance upsampling

depth estimation

self-attention

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 a novel deep neural model for text detection in document images. For robust text detection in noisy scanned documents, the advantages of multi-task learning are adopted by adding an auxiliary task of text enhancement. Namely, our proposed model is designed to perform noise reduction and text region enhancement as well as text detection. Moreover, we enrich the training data for the model with synthesized document images that are fully labeled for text detection and enhancement, thus overcome the insufficiency of labeled document image data. For the effective exploitation of the synthetic and real data, the training process is separated in two phases. The first phase is training only synthetic data in a fully-supervised manner. Then real data with only detection labels are added in the second phase. The enhancement task for the real data is weakly-supervised with information from their detection labels. Our methods are demonstrated in a real document dataset with performances exceeding those of other text detection methods. Moreover, ablations are conducted and the results confirm the effectiveness of the synthetic data, auxiliary task, and weak-supervision. Whereas the existing text detection studies mostly focus on the text in scenes, our proposed method is optimized to the applications for the text in scanned documents.

DUET: Detection Utilizing Enhancement for Text in Scanned or Captured Documents

Understanding video content and generating caption with context is an important and challenging task. Unlike prior methods that typically attempt to generate generic video captions without context, our architecture contextualizes captioning by infusing extracted information from relevant text data. 
We propose an end-to-end sequence-to-sequence model which generates video captions based on visual input, and mines relevant knowledge such as names and locations from contextual text. In contrast to previous approaches, we
do not preprocess the text further, and let the model directly learn to attend over it. Guided
by the visual input, the model is able to copy words from the contextual text via a pointer-generator
network, allowing to produce more specific video captions. 
We show competitive performance on the News Video Dataset and, through ablation studies, validate the efficacy of contextual video captioning as well as individual design choices in our model architecture.

Enriching Video Captions With Contextual Text


Contextualized representations from a pre-trained language model are central to achieve a high performance on downstream NLP task. The pre-trained BERT and A Lite BERT (ALBERT) models can be fine-tuned to give state-of-the-art results in sentence-pair regressions such as semantic textual similarity (STS) and natural language inference (NLI). Although BERT-based models yield the [CLS] token vector as a reasonable sentence embedding, the search for an optimal sentence embedding scheme remains an active research area in computational linguistics. This paper explores on sentence embedding models for BERT and ALBERT. In particular, we take a modified BERT network with siamese and triplet network structures called Sentence-BERT (SBERT) and replace BERT with ALBERT to create Sentence-ALBERT (SALBERT). We also experiment with an outer CNN sentence-embedding network for SBERT and SALBERT. We evaluate performances of all sentence-embedding models considered using the STS and NLI datasets. The empirical results indicate that our CNN architecture improves ALBERT models substantially more than BERT models for STS benchmark. Despite significantly fewer model parameters, ALBERT sentence embedding is highly competitive to BERT in downstream NLP evaluations.

Evaluation of BERT and ALBERT Sentence Embedding Performance on Downstream NLP Tasks


This paper studies the problem of Person Re-Identification (ReID)
for large-scale applications. Recent research efforts have been devoted
to building complicated part models, which introduce considerably
high computational cost and memory consumption, inhibiting its practicability
in large-scale applications. This paper aims to develop a novel learning
strategy to find efficient feature embeddings while maintaining the
balance of accuracy and model complexity. More specifically, we find
by enhancing the classical triplet loss together with cross-entropy
loss, our method can explore the hard examples and build a discriminant
feature embedding yet compact enough for large-scale applications.
Our method is carried out progressively using Bayesian optimization,
and we call it the Progressive Learning Algorithm (PLA). Extensive
experiments on three large-scale datasets show that our PLA is comparable
or better than the state-of-the-arts. Especially, on the challenging
Market-1501 dataset, we achieve Rank-1=94.7%/mAP=89.4% while saving
at least 30\% parameters than strong part models.

Human-Centric Parsing Network for Human-Object Interaction Detection

The advent of Generative Adversarial Network (GAN) architectures has given anyone the ability of generating incredibly realistic synthetic imagery. The malicious diffusion of GAN-generated images may lead to serious social and political consequences (e.g., fake news spreading, opinion formation, etc.). It is therefore important to regulate the widespread distribution of synthetic imagery by developing solutions able to detect them. In this paper, we study the possibility of using Benford’s law to discriminate GAN-generated images from natural photographs. Benford’s law describes the distribution of the most significant digit for quantized Discrete Cosine Transform (DCT) coefficients. Extending and generalizing this property, we show that it is possible to extract a compact feature vector from an image. This feature vector can be fed to an extremely simple classifier for GAN-generated image detection purpose.

On the use of Benford’s law to detect GAN-generated images 


Memetic evolution of training sets with adaptive radial basis kernels for support vector machines

The non-intrusive nature and high accuracy of face recognition algorithms have led to their successful deployment across multiple applications ranging from border access to mobile unlocking and digital payments. However, their vulnerability against sophisticated and cost-effective presentation attack mediums raises essential questions regarding its reliability. In the literature, several presentation attack detection algorithms are presented; however, they are still far behind from reality. The major problem with existing work is the generalizability against multiple attacks both in the seen and unseen setting. The algorithms which are useful for one kind of attack (such as print) perform unsatisfactorily for another type of attack (such as silicone masks). In this research, we have proposed a deep learning-based network termed as \textit{MixNet} to detect presentation attacks in cross-database and unseen attack settings. The proposed algorithm utilizes state-of-the-art convolutional neural network architectures and learns the feature mapping for each attack category. Experiments are performed using multiple challenging face presentation attack databases such as SMAD and Spoof In the Wild (SiW-M) databases. Extensive experiments and comparison with existing state of the art algorithms show the effectiveness of the proposed algorithm.

MixNet for Generalized Face Presentation Attack Detection

In my presentation, there are three main parts: background, literature and proposed method. My research work is about imbalanced data classification. The proposed method, oversampling the minority which is kind of data-level solutions to create balanced datasets. Motivated by the over-constraint and over-generation problems of traditional over-sampling solutions, I present my proposed method addressing these two problems accordingly.

Position-Aware Safe Boundary Interpolation Oversampling

—In this paper, we propose a dual memory model
inspired by neural science. Short-term memory processes the
data stream before integrating them into long-term memory,
which generalizes. The use case is learning the ability to recognize
handwriting. This begins with the learning of prototypical letters
. It continues throughout life and gives the individual the ability
to recognize increasingly varied handwriting. This second task
is achieved by incrementally training our dual-memory model.
We used a convolution network for encoding and random forests
as the memory model. Indeed, the latter have the advantage of
being easily enhanced to integrate new data and new classes.
Performances on the MNIST database are very encouraging
since they exceed 95% and the complexity of the model remains
reasonable

Dual-Memory Model for Incremental Learning:
The Handwriting Recognition Use Case



In this presentation, we will discuss the utilization of visual attention (saliency) for object detection and tracking and briefly summarise 1) How a visual attention map such as subjectness attention or saliency map and an objectness attention map can facilitate region proposal generation in a 2-stage object detector; 2) How a visual attention map can be used for tracking multiple objects using a sequential Monte Carlo probability hypothesis density (PHD) filter. The proposed neural network for simultaneously detect objects and generate objectness and subjectness maps to save computational power is also discussed in this presentation. The experiments are conducted on KITTI and DETRAC datasets. The use of visual attention and hierarchical features has shown a considerable improvement of ≈8% in object detection which effectively increased tracking performance by ≈4% on KITTIdataset.

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