AON: Towards Arbitrarily-Oriented Text Recognition

Recognizing text from natural images is a hot research topic in computer vision due to its various applications. Despite the enduring research of several decades on optical character recognition (OCR), recognizing texts from natural images is still a challenging task. This is because scene texts are often in irregular (e.g. curved, arbitrarily-oriented or seriously distorted) arrangements, which have not yet been well addressed in the literature. Existing methods on text recognition mainly work with regular (horizontal and frontal) texts and cannot be trivially generalized to handle irregular texts. In this paper, we develop the arbitrary orientation network (AON) to directly capture the deep features of irregular texts, which are combined into an attention-based decoder to generate character sequence. The whole network can be trained end-to-end by using only images and word-level annotations. Extensive experiments on various benchmarks, including the CUTE80, SVT-Perspective, IIIT5k, SVT and ICDAR datasets, show that the proposed AON-based method achieves the-state-of-the-art performance in irregular datasets, and is comparable to major existing methods in regular datasets.Comment: Accepted by CVPR201

Cross-modal Hashing with Semantic Deep Embedding

Cross-modal hashing has demonstrated advantages on fast retrieval tasks. It improves the quality of hash coding by exploiting semantic correlation across different modalities. In supervised cross-modal hashing, the learning of hash function replies on the quality of extracted features, for which deep learning models have been adopted to replace the traditional models based on handcraft features. All deep methods, however, have not sufficiently explored semantic correlation of modalities for the hashing process. In this paper, we introduce a novel end-to-end deep cross-modal hashing framework which integrates feature and hash-code learning into the same network. We take both between and within modalities data correlation into consideration, and propose a novel network structure and a loss function with dual semantic supervision for hash learning. This method ensures that the generated binary codes keep the semantic relationship of the original data points. Cross-modal retrieval experiments on commonly used benchmark datasets show that our method yields substantial performance improvement over several state-of-the-art hashing methods

Guardauto: A Decentralized Runtime Protection System for Autonomous Driving

Due to the broad attack surface and the lack of runtime protection, potential safety and security threats hinder the real-life adoption of autonomous vehicles. Although efforts have been made to mitigate some specific attacks, there are few works on the protection of the self-driving system. This paper presents a decentralized self-protection framework called Guardauto to protect the self-driving system against runtime threats. First, Guardauto proposes an isolation model to decouple the self-driving system and isolate its components with a set of partitions. Second, Guardauto provides self-protection mechanisms for each target component, which combines different methods to monitor the target execution and plan adaption actions accordingly. Third, Guardauto provides cooperation among local self-protection mechanisms to identify the root-cause component in the case of cascading failures affecting multiple components. A prototype has been implemented and evaluated on the open-source autonomous driving system Autoware. Results show that Guardauto could effectively mitigate runtime failures and attacks, and protect the control system with acceptable performance overhead

On the external photon fields in Fermi bright blazars

The external Compton (EC) model is used to study the high energy emission of some blazars, in which the external photon field is considered to dominate inverse Compton radiation. We explore the properties of external photon field through analyzing the FERMI LAT bright AGN sample within three months detection. In the sample, assuming the high energy radiation of low synchrotron peaked blazars from the EC process, we find that the external photon parameter Uext/\nuext may not be a constant. Calculating synchrotron and inverse Compton (IC) luminosity from the quasi-simultaneous broadband spectrum energy distributions (SEDs), we find that they have an approximately linear relation. This indicates that the ratio of external photon and magnetic energy density is a constant in the comoving frame, implying that the Lorentz factor of the emitting blob depends on external photon field and magnetic field. The result gives a strong constraint on the jet dynamic model.Comment: 7 pages, 2 figure

Transmission performance of 90°-bend optical waveguides fabricated in fused silica by femtosecond laser inscription

The L-shape waveguide was written in fused silica using a femtosecond laser with beam shaping. The guiding structure supports good light turning; 0.88 dB/turn was achieved at the silica-air interface. By using the finite-different time-domain method, the turn loss due to the turning structure and refractive index of the L-shape waveguide has been simulated. The results show that the proposed method has unprecedented flexibility in fabricating a 90°-bend waveguide