Italy

BatchNorm (BN) stabilizes network optimization by normalizing the activations during training and exploits mini-batch sample statistics. BN unfortunately suffers from performance degradation when the statistical estimates become unstable for small batch-size based tasks. This paper we propose WeightAlign: normalizing activations without using sample statistics. Instead of sample statistics, we re-parameterize the weights within a filter to arrive at correctly normalized activations.

ICPR 2020

WeightAlign: Normallizing Activations by Weight Alignment

batch size

sample statistics

weightalign

normalization

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 approach to adjust global image properties such as colour, saturation, and luminance using human-interpretable image enhancement curves, inspired by the Photoshop curves tool. Our method, dubbed neural CURve Layers (CURL), is designed as a multi-colour space neural retouching block trained jointly in three different colour spaces (HSV, CIELab, RGB) guided by a novel multi-colour
space loss. The curves are fully differentiable and are trained end-to-end for different computer vision problems including photo enhancement (RGB-to-RGB) and as part of the image signal processing pipeline for image formation (RAW-to-RGB). To demonstrate the effectiveness of CURL we combine this global image transformation block with a pixel-level (local) image multi-scale encoder-decoder backbone network. In an extensive experimental evaluation we show that CURL produces state of-the-art image quality versus recently proposed deep learning
approaches in both objective and perceptual metrics, setting new state-of-the-art performance on multiple public datasets. Our code is publicly available at: https://github.com/sjmoran/CURL.

CURL: Neural Curve Layers for Global Image Enhancement


Fake news often involves semantic manipulations across modalities such as image, text, location etc and requires the development of multimodal semantic forensics for its detection. Recent research has centered the problem around images, calling it image repurposing -- where a digitally unmanipulated image is semantically misrepresented by means of its accompanying multimodal metadata such as captions, location, etc. The image and metadata together comprise a multimedia package. The problem setup requires algorithms to perform multimodal semantic forensics to authenticate a query multimedia package using a reference dataset of potentially related packages as evidences. Existing methods are limited to using a single evidence (retrieved package), which ignores potential performance improvement from the use of multiple evidences. In this work, we introduce a novel graph neural network based model for multimodal semantic forensics, which effectively utilizes multiple retrieved packages as evidences and is scalable with the number of evidences. We compare the scalability and performance of our model against existing methods. Experimental results show that the proposed model outperforms existing state-of-the-art algorithms with an error reduction of up to 25%.

MEG: Multi-Evidence GNN for Multimodal Semantic Forensics

Video captioning automatically and accurately generates a textual description for a video. The typical methods following the encoder-decoder architecture directly utilize hidden states to predict words. Nevertheless, these methods do not amend the inaccurate hidden states before feeding those states into word prediction. This leads to a cascade of errors in generating word by word. In this paper, the context visual information-based deliberation network is proposed, abbreviated as CVI-DelNet. Its key idea is to introduce a deliberator into the encoder-decoder framework. The encoder-decoder first generates a raw hidden state sequence. Unlike the existing methods, the raw hidden state is no longer directly used for word prediction but is fed into the deliberator to generate the refined hidden state. The words are then predicted according to the refined hidden states and the contextual visual features. The results on two datasets show that the proposed method significantly outperforms the state-of-the-art methods.

Context Visual Information-based Deliberation Network for Video Captioning

Image classification is a fundamental task in computer vision. A variety of deep learning models based on the Convolutional Neural Network (CNN) architecture have proven to be an efficient solution. Numerous improvements have been proposed over the years, where broader, deeper, and denser networks have been constructed. However, the atomic operation for these models has remained a linear unit (single neuron). In this work, we pursue an alternative dimension by hypothesizing the atomic operation to be performed by a quadratic unit. We construct convolutional layers using quadratic neurons for feature extraction and subsequently use dense layers for classification. We perform analysis to quantify the implication of replacing linear neurons with quadratic units. Results show a keen improvement in classification accuracy with quadratic neurons over linear neurons.

CQNN: Convolutional Quadratic Neural Networks

Due to the recent breakthroughs of deep generative models, the fake faces, also known as deepfake which has been abused to deceive the general public, can be easily produced at scale and in very high fidelity. Many works focus on exploring various network architectures or various artifacts produced by deep generative models. Instead, in this work, we focus on the loss functions which have been shown to play a significant role in the context of face recognition. We perform a thorough study of several recent state-of-the-art losses commonly used in face recognition task for deepfake classification and detection since the current deepfake is highly related to face generation. With extensive experiments on the challenging FaceForensic++ and Celeb-DF datasets, the evaluation results provide a clear overview of the performance comparisons of different loss functions and generalization capability across different deepfake data.

An Experimental Evaluation of Recent Face Recognition Losses for Deepfake Detection

Recent advances in deep learning have made available
large, powerful convolutional neural networks (CNN) with
state-of-the-art performance in several real-world applications.
Unfortunately, these large-sized models have millions of parameters,
thus they are not deployable on resource-limited
platforms (e.g., where RAM is limited). Compression of CNNs
becomes therefore a critical problem to achieve memory-efficient
and possibly computationally faster model representations. In
this paper, we investigate the impact of lossy compression of
CNNs by weight pruning and quantization, and lossless weight
matrix representations based on source coding. We tested several
combinations of these techniques on four benchmark datasets
for classification and regression problems, achieving compression
rates up to 165 times, while preserving or improving the model
performance.

Compression strategies and space-conscious representations for deep neural networks

We introduce two methods of improving real-time object grasping performance from monocular colour images in an end-to-end CNN architecture. The first is the addition of an auxiliary task during model training (multi- task learning). Our multi-task CNN model improves grasping performance from a baseline average of 72.04% to 78.14% on the large Jacquard grasping dataset when performing a supplementary depth reconstruction task. The second is introducing a positional loss function that emphasises loss per pixel for secondary parameters (gripper angle and width) only on points of an object where a successful grasp can take place. This increases performance from a baseline average of 72.04% to 78.92% as well as reducing the number of training epochs required. These methods can be also performed in tandem resulting in a further performance increase to 79.12%, while maintaining sufficient inference speed to afford real-time grasp processing.

Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss

This paper proposes a sketch-based approach to the community detection problem which clusters the full graph through the use of an informative and concise sketch. The reduced sketch is built through an effective sampling approach which selects few nodes that best represent the complete graph and operates on a pairwise node similarity measure based on the average commute time. After sampling, the proposed algorithm clusters the nodes in the sketch, and then infers the cluster membership of the remaining nodes in the full graph based on their aggregate similarity to nodes in the partitioned sketch. By sampling nodes with strong representation power, our approach can improve the success rates over full graph clustering. In challenging cases with large node degree variation, our approach not only maintains competitive accuracy with full graph clustering despite using a small sketch, but also outperforms existing sampling methods. The use of a small sketch allows considerable storage savings, and computational and timing improvements for further analysis such as clustering and visualization. We provide numerical results on synthetic data based on the homogeneous, heterogeneous and degree corrected versions of the stochastic block model, as well as experimental results on real-world data.

Sketch-based Community Detection via Representative Node Sampling


The aggressive growth of digital data threatens to exceed the capacity of conventional storage devices. The need for new means to store digital information has brought great interest in novel solutions as it is DNA, whose biological properties allow the storage of information at a high density and preserve it without any information loss for hundreds of years when stored under speciﬁc conditions. Despite being a promising solution, DNA storage faces two major obstacles: the large cost of synthesis and the high error rate introduced during sequencing. While most of the works focus on adding redundancy aiming for effective error correction, this work combines noise resistance to minimize the impact of the errors in the decoded data and post-processing to further improve the quality of the decoding.

Robust image coding on synthetic DNA: Reducing sequencing noise with inpainting


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.

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