Current research on embodied AI mainly focuses on stand-alone tasks. Instead, we aim at bridging recent findings on embodied exploration and image captioning.
We devise a novel setting involving the exploration of a photorealistic indoor environment while describing the current RGB observation, and we call it Explore and Explain.
We present a novel solution involving curiosity-driven exploration and we apply a captioning algorithm to describe relevant objects along the trajectory of the agent.

Explore and Explain: Self-Supervised Navigation and Recounting


Handwritten Text Recognition (HTR) in free-layout pages is a valuable yet challenging task which aims to automatically understand handwritten texts. State-of-the-art approaches in this field usually encode input images with Convolutional Neural Networks, whose kernels are typically defined on a fixed grid and focus on all input pixels independently. However, this is in contrast with the sparse nature of handwritten pages, in which only pixels representing the ink of the writing are useful for the recognition task. Furthermore, the standard convolution operator is not explicitly designed to take into account the great variability in shape, scale, and orientation of handwritten characters. To overcome these limitations, we investigate the use of deformable convolutions for handwriting recognition. The kernel of this type of convolution deforms according to the content of the neighborhood, and can therefore be more adaptable to geometric variations and other deformations of the text. Experiments conducted on the IAM and RIMES datasets demonstrate that the use of deformable convolutions is a promising direction for the design of novel architectures for handwritten text recognition.

Watch Your Strokes: Improving Handwritten TextRecognition with Deformable Convolutions

**Session Chairs**: prof. Federico Pernici, prof. Fabio Poiesi

OS T3.2: Computer Vision, Robotics and Tracking

technical paper

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. 


Silvia Cascianelli

3

SHORT BIO

Presentations

Explore and Explain: Self-Supervised Navigation and Recounting

Watch Your Strokes: Improving Handwritten TextRecognition with Deformable Convolutions

Explore and Explain: Self-Supervised Navigation and Recounting

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