Sketchformer is a novel transformer-based representation for encoding free-hand sketches input in a vector form, i.e. as a sequence of strokes. Sketchformer effectively addresses multiple tasks: sketch classification, sketch based image retrieval (SBIR), and the reconstruction and interpolation of sketches. We report several variants exploring continuous and tokenized input representations, and contrast their performance. Our learned embedding, driven by a dictionary learning tokenization scheme, yields state of the art performance in classification and image retrieval tasks, when compared against baseline representations driven by LSTM sequence to sequence architectures: SketchRNN and derivatives. We show that sketch reconstruction and interpolation are improved significantly by the Sketchformer embedding for complex sketches with longer stroke sequences.

Detecting anomalous activity in video surveillance often suffers from limited availability of training data. Transfer learning may close this gap, allowing to use existing annotated data from some source domain. However, analyzing the source feature space in terms of its potential for transfer of learning to another context is still to be investigated. This paper reports a study on video anomaly detection, focusing on the analysis of feature embeddings of pre-trained CNNs with the use of novel cross-domain generalization measures that allow to study how source features generalize for different target video domains. This generalization analysis represents not only a theoretical approach, can be useful in practice as a path to understand which datasets allow better transfer of knowledge. Our results confirm this, achieving better anomaly detectors for video frames and allowing analysis of transfer learning's positive and negative aspects.

We propose and evaluate several deep network architectures for measuring the similarity between sketches and photographs, within the context of the sketch based image retrieval (SBIR) task.We study the ability of our networks to generalize across diverse object categories from limited training data, and explore in detail strategies for weight sharing, pre-processing, data augmentation and dimensionality reduction.In addition to a detailed comparative study of network configurations, we contribute by describing a hybrid multi-stage training network that exploits both contrastive and triplet networks to exceed state of the art performance on several SBIR benchmarks by a significant margin.Datasets and models are available at www.cvssp.org.

We present an efficient representation for sketch based image retrieval (SBIR) derived from a triplet loss convolutional neural network (CNN). We treat SBIR as a cross-domain modelling problem, in which a depiction invariant embedding of sketch and photo data is learned by regression over a siamese CNN architecture with half-shared weights and modified triplet loss function. Uniquely, we demonstrate the ability of our learned image descriptor to generalise beyond the categories of object present in our training data, forming a basis for general cross-category SBIR. We explore appropriate strategies for training, and for deriving a compact image descriptor from the learned representation suitable for indexing data on resource constrained e. g. mobile devices. We show the learned descriptors to outperform state of the art SBIR on the defacto standard Flickr15k dataset using a significantly more compact (56 bits per image, i. e. ≈ 105KB total) search index than previous methods. Datasets and models are available from the CVSSP datasets server at www.cvssp.org.