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LEAR and XRCE’s participation to Visual Concept Detection Task – ImageCLEF 2010
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| Thomas Mensink, Gabriela Csurka, Florent Perronnin, Jorge Sanchez, Verbeek Jakob |
| CLEF 2010 - Conference on Multilingual and Multimodal Information Access evaluation, Padua, Italy , 20-23 September 2010 |
In this paper we present the common effort of Lear and XRCE for the lmageCLEF Visual Concept Detection and Annotation Task. We first sought to combine our individual stale-of-the-art approaches: the Fisher vector image representation, with the TagProp method for image auto-annotation. Our second motivation was to investigate the annotation performance by using extra information in the form of provided Flickr-tags.
The results show that using the Flickr-tags in combination with visual features improves the results of any method using only visual features. Our winning system, an early-fusion linear-SVM classifier, trained on visual and flicks-lags features, obtains 45.53 in mean Average Precision (mAP), almost a 5% absolute improvement compared to the best visual-only system. Our best visual-only system obtains 39.0% mAP, and is close to the best visual-only system. It is a late-fusion linear-SVM classifier, trained on two types of visual features (SIFT and color). The performance of TagProp is close to our SVM classifiers.
The methods presented in this paper, are all scalable to large datasets and/or many concepts. This is due to the fast FK framework for image representation, and due to the classifiers. The linear SVM classifier has proven lo scale well for large datasets. The k-MN approach of TagProp is interesting in this respect since it requires only 2 parameters per concept.
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
VISION
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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