Professor Christophoros Nikou

Keynote Address

“Identifying people in images by classification of visual attributes ”

When we are interested in providing a description of an object or a human, we tend to use visual attributes to accomplish this task. For example, a laptop can have a wide screen, a silver color, and a brand logo, whereas a human can be tall, female, wearing a blue t-shirt and carrying a backpack. Visual attributes in computer vision are equivalent to the adjectives in our speech. We rely on visual attributes since (i) they enhance our understanding by creating an image in our head of what this object or human looks like; (ii) they narrow down the possible related results when we want to search for a product online or when we want to provide a suspect description; (iii) they can be composed in different ways to create descriptions; (iv) they generalize well as with some finetuning they can be applied to recognize objects for different tasks; and (v) they are a meaningful semantic representation of objects or humans that can be understood by both computers and humans. However, effectively predicting the corresponding visual attributes of a human given an image remains a challenging task. In real-life scenarios, images might be of low-resolution, humans might be partially occluded in cluttered scenes, or there might be significant pose variations.

In this talk, a deep learning method to solve multiple binary classification tasks will be described (e.g. “Provide the images showing an old man standing in front of a coffee-shop”). The method performs end-to-end learning by feeding a single convolutional network (ConvNet) with the entire image of a human. It will be demonstrated how both multi-task and curriculum learning may be exploited in that framework and state-of-the-art results on publicly available datasets of humans standing with their full-body visible will be discussed.

Computer vision, Machine learning, Curriculum learning; Multi-task classification; Visual attributes; Deep learning; Convolutional networks

Research Field Keywords

Computer vision; pattern recognition; image processing; image analysis; biomedical imaging


  • Associate Professor, University of Ioannina (Greece), Department of Computer Science and Engineering, 2013 – now
    (Lecturer 2004-2009, Assistant Professor 2009-2013)
  • Visiting Professor, University of Houston (USA), Department of Computer Science, 2015-2016


  • Doctorate: PhD in Image Processing and Computer Vision, University of Strasbourg, France, 1999
  • Master’s: DEA in Photonics and Image Processing, , University of Strasbourg, France, 1995
  • Bachelor’s: Diploma in Electrical Engineering, University of Thessaloniki, Greece, 1994

Additional Information

  • IEEE Senior Member since 2011
  • General Chair of the 2018 IEEE International Conference on Image Processing (ICIP 2018), 7-10 October 2018, Athens, Greece