Sketch Recognition with Few Examples
Our systems perform self-learning by automatically extending a very small set of labeled examples with new examples extracted from unlabeled sketches.
Our evaluations demonstrate that both taxonomy and distributional similarity serve as useful sources of information for attribute nomination, and our methods can effectively exploit them.
Our experiments show that our multimodal
approach supported by bagging compares favorably to the state of the art in presence of detrimental factors such as cross-talk,
environmental noise, and data imbalance.
Stereo techniques cannot easily recover reflecting and textureless surfaces by using traditional local regularizers. We propose to regularize over larger distances using object-category specific disparity proposals (displets) which we sample using inverse graphics techniques based on a sparse disparity estimate and a semantic segmentation of the image.
Deep Stroke-based Sketched Symbol Reconstruction and Segmentation
We propose a neural network model that segments symbols into stroke-level components. Our segmentation framework has two main elements: a fixed feature extractor and a Multilayer Perceptron (MLP) network that identifies a component based on the feature
Active Learning for Sketch Recognition
Our results imply that the Margin based informativeness measure consistently outperforms other measures. We also show that active learning brings definitive advantages in challenging databases when accompanied with powerful feature representations.
The system indexes collection data with over 30 visual features describing color,
edge, motion, and semantic information. Resulting feature data is stored in ADAM, an efficient database system
optimized for fast retrieval.
This paper presents the second version of the IMOTION
system, a sketch-based video retrieval engine supporting multiple query
paradigms. For the second version, the functionality and the usability of the system have been improved.
This paper introduces iAutoMotion, an autonomous video
retrieval system that requires only minimal user input. It is based on the
video retrieval engine IMOTION.
Often, individuals with an Autism Spectrum Condition (ASC) have difficulties in interpreting verbal and non-verbal communication cues during social interactions. We develop a platform for children who have an ASC to learn emotion expression and recognition, through play in the virtual world.
DPFrag is an efficient, globally optimal fragmentation method that learns segmentation parameters from data and produces fragmentations by combining primitive recognizers in a dynamic-programming framework. The fragmentation is fast and doesn’t require laborious and tedious parameter tuning. In experiments, it beat state-of-the-art methods on standard databases with only a handful of labeled examples.