Italy


Our daily life is surrounded by textual information. Nowadays, the automatic collection of textual information becomes possible owing to the drastic improvement of scene text detectors and recognizer. The purpose of this paper is to conduct a large-scale survey of co-occurrence between visual objects (such as book and car) and scene texts with a large image dataset and a state-of-the-art scene text detector and recognizer. Especially, we focus on the function of &quot;label&quot; texts, which are attached to objects for detailing the objects. By analyzing co-occurrence between objects and scene texts, it is possible to observe the statistics about the label texts and understand how the scene texts will be useful for recognizing the objects and vice versa.

ICPR 2020

Label or Message: A Large-Scale Experimental Survey of Texts and Objects Co-Occurrence

scene text

object recognition

computer vision

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. 



Our daily life is surrounded by textual information. Nowadays, the automatic collection of textual information becomes possible owing to the drastic improvement of scene text detectors and recognizer. The purpose of this paper is to conduct a large-scale survey of co-occurrence between visual objects (such as book and car) and scene texts with a large image dataset and a state-of-the-art scene text detector and recognizer. Especially, we focus on the function of "label" texts, which are attached to objects for detailing the objects. By analyzing co-occurrence between objects and scene texts, it is possible to observe the statistics about the label texts and understand how the scene texts will be useful for recognizing the objects and vice versa.

technical paper


The age of social media insists on developing devices that are able to capture and share ones’ moments with high fidelity. Handheld devices such as smartphones with wide-angle cameras have shown the current trend in mobile photography. Although one can take great delight in a wide field of view through modern cameras, nearby objects or faces may be distorted significantly. Recent works have obtained impressive results in this research area, but there is still a tradeoff between image quality and processing time to consider. This work introduces an adaptive polynomial model that automatically selects faces and performs image distortion correction. Since the photos are processed locally, faces are undistorted, and the background is close to the original state. Unlike other content-aware based methods which rely on heavy computing components and high image resolution, our model is suitable for mobile devices to tackle face distortion issue in selfie photos.

An Adaptive Model for Face Distortion Correction


Image-based multiplexed in situ RNA detection makes it possible to map the spatial 
gene expression of hundreds to thousands of genes in parallel, and thus discern at the same time a large numbers of different cell types to better understand tissue
development, heterogeneity, and disease. Fluorescent signals are detected over multiple fluorescent channels and imaging rounds and decoded in order to identify RNA molecules in their morphological context. Here we present a graph-based decoding approach that models the decoding process as a network flow problem jointly optimizing observation likelihoods and distances of signal detections, thus achieving robustness with respect to noise and spatial jitter of the fluorescent signals.

Graph-based Image Decoding for Multiplexed In Situ RNA Detection


Video object detection is challenging and essential in practical applications, such as surveillance cameras for traffic control and public security.
Unlike the video in natural scenes, 
the surveillance video tends to contain dense and small objects (typically vehicles) in their appearances.
Therefore, existing methods for surveillance object detection utilize still-image object detection approaches with rich feature extractors at the expense of their run-time speeds. 
The run-time speed, however, becomes essential when the video is being streamed.
In this paper, we exploit temporal information in videos to enrich the feature maps, proposing the first temporal attention based external memory network for the live stream of video.
Extensive experiments on real-world traffic surveillance benchmarks demonstrate the real-time performance of the proposed model while keeping comparable accuracy with state-of-the-art.

Temporal Feature Enhancement Network with External Memory for Object Detection in Surveillance Video

Despite the impressive advancement achieved in object detection, the detection performance of small object is still far from satisfactory due to the lack of sufficient detailed appearance to distinguish it from similar objects. Inspired by the positive effects of super-resolution for object detection, we propose a framework that can be incorporated with detector networks to improve the performance of small object detection, in which the low-resolution image is super-resolved via generative adversarial network (GAN) in an unsupervised manner. In our method, the super-resolution network and the detection network are trained jointly. In particular, the detection loss is back-propagated into the SR network during training to facilitate detection. 


Small Object Detection Leveraging on Simultaneous
Super-resolution


With the proliferation of remote sensing images, how to segment buildings more accurately in remote sensing images is a critical challenge. First, the high resolution leads to blurred boundaries in the extracted building maps. Second, the similarity between buildings and background results in intraclass inconsistency. To address these two problems, we propose an UNet-based network named Context-Transfer-UNet (CT-UNet). Specifically, we design Dense Boundary Block (DBB). Dense
Block utilizes reuse mechanism to refine features and increase recognition capabilities. Boundary Block introduces the low-level spatial information to solve the fuzzy boundary problem. Then,to handle intra-class inconsistency, we construct Spatial Channel Attention Block (SCAB). Finally, we propose a novel loss function to enhance the purpose of loss by adding evaluation indicator.

CT-UNet: An Improved Neural Network Based on U-Net for Building Segmentation in Remote Sensing Images

Estimating the actual head orientation from 2D images, with regard to its three degrees of freedom, is a well known problem that is highly significant for a large number of applications involving head pose knowledge. Consequently, this topic has been tackled by a plethora of methods and algorithms the most part of which exploits neural networks. Machine learning methods, indeed, achieve accurate head rotation values yet require an adequate training stage and, to that aim, a relevant number of positive and negative examples. In this paper we take a different approach to this topic by using fractal coding theory and particularly Partitioned Iterated Function Systems to extract the fractal code from the input head image and to compare this representation to the fractal code of a reference model through Hamming distance. According to experiments conducted on both the BIWI and the AFLW2000 databases, the proposed PIFS based head pose estimation method provides accurate yaw/pitch/roll angular values, with a performance approaching that of state of the art of machine-learning based algorithms and exceeding most of non-training based approaches.

HP2IFS: Head Pose estimation exploiting Partitioned
Iterated Function Systems

The detection of small objects is of particular interest in many real applications. In this paper, we propose STDnet-ST, a novel approach to small object detection in video using spatial information operating alongside temporal video information. STDnet-ST is an end-to-end spatio-temporal convolutional neural network that detects small objects over time and correlates pairs of the top-ranked regions with the highest likelihood of containing small objects. This architecture links the small objects across the time as tubelets, being able to dismiss unprofitable object links in order to provide high-quality tubelets. STDnet-ST achieves state-of-the-art results for small objects on the publicly available USC-GRAD-STDdb and UAVDT video datasets.

Correlation-Based ConvNet for Small Object Detection in Videos


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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Label or Message: A Large-Scale Experimental Survey of Texts and Objects Co-Occurrence

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