Dendrogram distance: an evaluation metric for generative networks using hierarchical clustering

We present a novel metric for generative modeling evaluation, focusing primarily on generative networks. The method uses dendrograms to represent real and fake data, allowing for the divergence between training and generated samples to be computed. This metric focus on mode collapse, targeting generators that are not able to capture all modes in the training set. To evaluate the proposed method it is introduced a validation scheme based on sampling from real datasets, therefore the metric is evaluated in a controlled environment and proves to be competitive with other state-of-the-art approaches

Sketch-an-Anchor: Sub-epoch Fast Model Adaptation for Zero-shot Sketch-based Image Retrieval

Sketch-an-Anchor is a novel method to train state-of-the-art Zero-shot Sketch-based Image Retrieval (ZSSBIR) models in under an epoch. Most studies break down the problem of ZSSBIR into two parts: domain alignment between images and sketches, inherited from SBIR, and generalization to unseen data, inherent to the zero-shot protocol. We argue one of these problems can be considerably simplified and re-frame the ZSSBIR problem around the already-stellar yet underexplored Zero-shot Image-based Retrieval performance of off-the-shelf models. Our fast-converging model keeps the single-domain performance while learning to extract similar representations from sketches. To this end we introduce our Semantic Anchors -- guiding embeddings learned from word-based semantic spaces and features from off-the-shelf models -- and combine them with our novel Anchored Contrastive Loss. Empirical evidence shows we can achieve state-of-the-art performance on all benchmark datasets while training for 100x less iterations than other methods

Sketchformer: Transformer-based Representation for Sketched Structure

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.Comment: Accepted for publication at CVPR 202

Color description of low resolution images using fast bitwise quantization and border-interior classification

Image classification often require preprocessing and feature extraction steps that are directly related to the accuracy and speed of the whole task. In this paper we investigate color features extracted from low resolution images, assessing the influence of the resolution settings on the final classification accuracy. We propose a border-interior classification extractor with a logarithmic distance function in order to maintain the discrimination capability in different resolutions. Our study shows that the overall computational effort can be reduced in 98%. Besides, a fast bitwise quantization is performed for its efficiency on converting RGB images to one channel images. The contributions can benefit many applications, when dealing with a large number of images or in scenarios with limited network bandwidth and concerns with power consumption.FAPESP (grants # 10/19159-1 and 11/22749-8)CNPq (grant # 482760/2012-5