Transductive Multi-View Zero-Shot Learning

(c) 2012. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms

Fine-grained sketch-based image retrieval by matching deformable part models

(c) 2014. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.© 2014. The copyright of this document resides with its authors. An important characteristic of sketches, compared with text, rests with their ability to intrinsically capture object appearance and structure. Nonetheless, akin to traditional text-based image retrieval, conventional sketch-based image retrieval (SBIR) principally focuses on retrieving images of the same category, neglecting the fine-grained characteristics of sketches. In this paper, we advocate the expressiveness of sketches and examine their efficacy under a novel fine-grained SBIR framework. In particular, we study how sketches enable fine-grained retrieval within object categories. Key to this problem is introducing a mid-level sketch representation that not only captures object pose, but also possesses the ability to traverse sketch and image domains. Specifically, we learn deformable part-based model (DPM) as a mid-level representation to discover and encode the various poses in sketch and image domains independently, after which graph matching is performed on DPMs to establish pose correspondences across the two domains. We further propose an SBIR dataset that covers the unique aspects of fine-grained SBIR. Through in-depth experiments, we demonstrate the superior performance of our SBIR framework, and showcase its unique ability in fine-grained retrieval

Learning Multimodal Latent Attributes

Abstract—The rapid development of social media sharing has created a huge demand for automatic media classification and annotation techniques. Attribute learning has emerged as a promising paradigm for bridging the semantic gap and addressing data sparsity via transferring attribute knowledge in object recognition and relatively simple action classification. In this paper, we address the task of attribute learning for understanding multimedia data with sparse and incomplete labels. In particular we focus on videos of social group activities, which are particularly challenging and topical examples of this task because of their multi-modal content and complex and unstructured nature relative to the density of annotations. To solve this problem, we (1) introduce a concept of semi-latent attribute space, expressing user-defined and latent attributes in a unified framework, and (2) propose a novel scalable probabilistic topic model for learning multi-modal semi-latent attributes, which dramatically reduces requirements for an exhaustive accurate attribute ontology and expensive annotation effort. We show that our framework is able to exploit latent attributes to outperform contemporary approaches for addressing a variety of realistic multimedia sparse data learning tasks including: multi-task learning, learning with label noise, N-shot transfer learning and importantly zero-shot learning

Ideal switching effect in periodic spin-orbit coupling structures

An ideal switching effect is discovered in a semiconductor nanowire with a spatially-periodic Rashba structure. Bistable `ON' and `OFF' states can be realized by tuning the gate voltage applied on the Rashba regions. The energy range and position of `OFF' states can be manipulated effectively by varying the strength of the spin-orbit coupling (SOC) and the unit length of the periodic structure, respectively. The switching effect of the nanowire is found to be tolerant of small random fluctuations of SOC strength in the periodic structure. This ideal switching effect might be applicable in future spintronic devices.Comment: 4 pages and 4 figure

Chaplygin Gravitodynamics

We consider a new approach for gravity theory coupled to Chaplygin matter in which the {\it{relativistic}} formulation of the latter is of crucial importance. We obtain a novel form of matter with dust like density $(\sim (volume)^{-1})$ and negative pressure. We explicitly show that our results are compatible with a relativistic generalization of the energy conservation principle, derived here.Comment: Title changed, Revised version,N o change in conclusions, Journal ref.: MPL A21 (2006)1511-151