Italy

Fashion analysis is an attractive domain for vision research due to its direct applications in e-commerce contexts.
However, fashion datasets are commonly rather demanding, as both objects and attributes tend to be fine-grained and thus result in very long-tailed datasets.
Furthermore, relationships between objects and attributes are often dense, but are crucial for the performance of fashion applications.
In this paper, we propose to generate scene graphs for existing fashion datasets.
By detecting relationships between fashion objects, their parts, and their attributes we gain a better understanding of the scenes.
As no current fashion dataset provides scene graphs, we generate relationships between fashion objects from existing annotations.
The output is post-processed and filtered to generate a meaningful scene graph for each image.
In the experiments, we can show existing applications like image retrieval benefiting from the scene graph understanding.
We first evaluate the accuracy of the generated scene graphs.
Then, we employ scene graphs to fashion image retrieval in order to showcase their performance in real applications.
The results show various benefits for fashion applications by exploiting scene graph knowledge.
The resources and model for the proposed method are available on GitHub (https://github.com/shabnamsadegh/FashionGraph).

ICPR 2020

FashionGraph: understanding fashion data using scene graph generation

fashion image retrieval

fashion data analysis

scene graph

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


Lecture videos are rapidly becoming an invaluable source of information for students across the globe. Given the large number of online courses currently available, it is important to condense the information within these videos into a compact yet representative summary that can be used for search-based applications. We propose a framework to summarize whiteboard lecture videos by finding feature representations of detected handwritten content regions to determine unique content. We investigate multi-scale histogram of gradients and embeddings from deep metric learning for feature representation. We explicitly handle occluded, growing and disappearing handwritten content. Our method is capable of producing two kinds of lecture video summaries - the unique regions themselves or so-called key content and keyframes (which contain all unique content in a video segment). We use weighted spatio-temporal conflict minimization to segment the lecture and produce keyframes from detected regions and features. We evaluate both types of summaries and find that we obtain state-of-the-art peformance in terms of number of summary keyframes while our unique content recall and precision are comparable to state-of-the-art.

Automated Whiteboard Lecture Video Summarization by Content Region Detection and Representation

The perspective-n-point (PnP) problem is of fundamental importance in computer vision. A global optimality condition for PnP that is independent of a particular rotation parameterization was recently developed by Nakano. This paper puts forward a direct least squares, algebraic PnP solution that extends Nakano's work by combining his optimality condition with the modified Rodrigues parameters (MRPs) for parameterizing rotation. The result is a system of polynomials that is solved using the Gr\obner basis approach. An MRP vector has twice the rotational range of the classical Rodrigues (i.e.

A Globally Optimal Method for the PnP Problem with MRP Rotation Parameterization


This paper tackles automated screening of Retinopa- thy of Prematurity (ROP), one of the most common causes of visual loss in childhood. Clinically, ROP screening per case requires multiple color fundus images capturing different zones of the premature retina. A desirable model shall not only make a decision at the case level, but also pinpoint which instances and what part of the instances are responsible for the decision. This paper makes the first attempt to accomplish three tasks, i.e. ROP case classification, instance selection and abnormality localization in a unified framework. To that end, we propose a new model that effectively combines instance-attention based deep multiple instance learning (MIL) and spatial attention (SA). The propose model, which we term MIL-SA, identifies positive instances in light of their contributions to case-level decision. Meanwhile, abnormal regions in the identified instances are automatically localized by the SA mechanism. Moreover, MIL-SA is learned from case-level binary labels exclusively, and in an end-to-end manner. Experiments on a large clinical dataset of 2,186 cases with 11,053 fundus images show the viability of the proposed model for all the three tasks.

Deep Multiple Instance Learning with Spatial Attention for ROP Case Classification, Instance Selection and Abnormality Localization


Sensitive gravitational wave (GW) detectors such as that of Laser Interferometer Gravitational-wave Observatory (LIGO) realize the direct observation of GW signals that confirm Einstein’s general theory of relativity. However, it remains chal- lenges to quickly detect faint GW signals from a large number of time series with background noise under unknown probability distributions. Traditional methods such as matched-filtering in general assume Additive White Gaussian Noise (AWGN) and are far from being real-time due to its high computational complexity. To avoid these weaknesses, one-dimensional (1D) Convolutional Neural Networks (CNNs) are introduced to achieve fast online detection in milliseconds but do not have enough consideration on the trade-off between the frequency and time features, which will be revisited in this paper through data pre-processing and subsequent two-dimensional (2D) CNNs during offline training to improve the online detection sensitivity.
In this work, the input data is pre-processed to form a 2D spec- trum by Short-time Fourier transform (STFT), where frequency features are extracted without learning. Then, carrying out two 1D convolutions across time and frequency axes respectively, and concatenating the time-amplitude and frequency-amplitude feature maps with equal proportion subsequently, the frequency and time features are treated equally as the input of our following two-dimensional CNNs. The simulation of our above ideas works on a generated data set with uniformly varying SNR (2-17), which combines the GW signal generated by PYCBC and the background noise sampled directly from LIGO. Satisfying the real-time online detection requirement without noise distribution assumption, the experiments of this paper demonstrate better performance on average compared to that of 1D CNNs, especially in the cases of lower SNR (4-9).

Improving Gravitational Wave Detection with 2D Convolutional Neural Networks


Functional MRI based on blood oxygenation level dependent (BOLD) contrast is well established as a neuroimaging technique for detecting neural activity in the cortex
of the human brain. Recent studies have shown that variations of BOLD signals in white matter are also related to neural activities both in resting state and under functional loading. We develop a comprehensive framework of detecting task-specific fiber pathways. We not only study fiber tracts as open curves with different physical features (shape, scale, orientation and position), but also incorporate the BOLD signals associated with them to find stimulus-relevant pathways. Specifically, we propose a novel Riemannian metric, which is a weighted sum of distances in product space of shapes and functions. This metric provides both a cost function for registration and a proper distance for comparison. Experimental results on real data have shown that we can cluster fiber pathways correctly by evaluating correlations
between BOLD signals and stimuli, temporal variations and power spectra of them.

A Riemannian Framework for Detecting Stimulus-Relevant Fiber Pathways


Our work is about simultaneously pixel-level retinal lesion segmentation and image-level disease classification.

Learn to Segment Retinal Lesions and Beyond

A network to solve the segmentation of ultra-high resolution images

UHRSNet: A Semantic Segmentation NetworkSpecifically for Ultra-High-Resolution Images

One of the significant challenges of deep neural networks is that the complex nature of the network prevents human comprehension of the outcome of the network. Consequently, the applicability of complex machine learning models is limited in the safety-critical domains, which incurs risk to life and property. To fully exploit the capabilities of complex neural networks, we propose a non-intrusive interpretability technique that uses the input and output of the model to generate a saliency map. The method works by empirically optimizing a randomly initialized input mask by localizing and weighing individual pixels according to their sensitivity towards the target class. Our experiments show that the proposed model interpretability approach performs better than the existing saliency map-based approaches methods at localizing the relevant input pixels. 

Furthermore, to obtain a global perspective on the target-specific explanation, we propose a saliency map reconstruction approach to generate acceptable variations of the salient inputs from the space of input data distribution for which the model outcome remains unaltered. Experiments show that our interpretability method can reconstruct the salient part of the input with a classification accuracy of 89%.

A generalizable saliency-map based interpretation of model outcome

This study attempts to use crowdsourcing to facilitate the operation of pattern-recognition-based video surveillance systems at an early stage. Target events (i.e. events to be detected during surveillance) are not frequently observed in recorded video, so achieving reliable surveillance on the basis of machine learning requires a sufficient amount of target data. Acquiring sufficient data is time-consuming. However, operating unreliable surveillance systems can induce many false alarms. Crowdsourcing is introduced to address this problem by verifying the unreliable results in data-driven surveillance. Experimental simulation conducted using monitoring video of Japanese black beef cattle demonstrates that crowdsourced verification successfully reduced false alarms in calving detection systems.

Crowdsourced Verification for Operating Calving Surveillance Systems at an Early Stage

This paper tackles on building a pattern recognition system that detects whether a pair of Japanese black beefs captured in a given image region is in a “mounting” action, which is known to be a sign critically important to be detected for cattle farmers before artificial insemination. The “mounting” action refers to a cattle’s action where a cow bends over another cow usually when either cow is in estrus. Although a pattern recognition-based approach for detecting such an action would be appreciated as being low-cost and robust, it had not been discussed much due to the complexity of the system architecture, unavailability of datasets, etc. This study presents i) our image dataset construction technique that exploits both object detection algorithm and crowdsourcing for collecting cattle pair images with labels of either “mounting” or not; and ii) a system for detecting the mounting action from any given image of a cattle pair, developed based on the dataset. Starting with an algorithm for extracting regions of cattle pairs from a video frame based on the intersection of single cattle regions, we then designed our crowdsourcing microtask in which crowd workers were given simple guidelines to annotate mounting-action-relevant labels to the extracted regions, to finally obtain a dataset. We also introduce our tandem-layered pattern recognition system trained with the dataset. The system is comprised of two serially-connected machine learning components, and is capable of more robustly detecting mounting actions even with a small amount of training data than a normal end-to-end neural network. Experimental comparisons demonstrated that our detection system was capable of detecting estrus with a precision rate of 80% and a recall rate of 76%.

Premium content

FashionGraph: understanding fashion data using scene graph generation

Downloads

Next from ICPR 2020

Automated Whiteboard Lecture Video Summarization by Content Region Detection and Representation

Similar lecture

Using Scene Graphs for Detecting Visual Relationships