AutoCorrect: Deep Inductive Alignment of Noisy Geometric Annotations

We propose AutoCorrect, a method to automatically learn object-annotation alignments from a dataset with annotations affected by geometric noise. The method is based on a consistency loss that enables deep neural networks to be trained, given only noisy annotations as input, to correct the annotations. When some noise-free annotations are available, we show that the consistency loss reduces to a stricter self-supervised loss. We also show that the method can implicitly leverage object symmetries to reduce the ambiguity arising in correcting noisy annotations. When multiple object-annotation pairs are present in an image, we introduce a spatial memory map that allows the network to correct annotations sequentially, one at a time, while accounting for all other annotations in the image and corrections performed so far. Through ablation, we show the benefit of these contributions, demonstrating excellent results on geo-spatial imagery. Specifically, we show results using a new Railway tracks dataset as well as the public INRIA Buildings benchmarks, achieving new state-of-the-art results for the latter.Comment: BMVC 2019 (Spotlight

Registration of SD-OCT en-face images with color fundus photographs based on local patch matching

Registration of multi-modal retinal images is very significant to integrate information gained from different modalities for a reliable diagnosis of retinal diseases by ophthalmologists. However, accurate image registration is a challenging, we propose an algorithm for registration of summed-voxel projection images (SVPIs) with color fundus photographs (CFPs) based on local patch matching. SVPIs are evenly split into 16 local image blocks for extracting matching point pairs by searching local maximization of the similarity function. These matching point pairs are used for a coarse registration and then a search region of feature matching points is redefined for a more accurate registration. The performance of our registration algorithm is tested on a series of datasets including 3 normal eyes and 20 eyes with age-related macular degeneration. The experiment demonstrates that the proposed method can achieve accurate registration results (the average of root mean square error is 128μm)

Auto-AVSR: Audio-Visual Speech Recognition with Automatic Labels

Audio-visual speech recognition has received a lot of attention due to its robustness against acoustic noise. Recently, the performance of automatic, visual, and audio-visual speech recognition (ASR, VSR, and AV-ASR, respectively) has been substantially improved, mainly due to the use of larger models and training sets. However, accurate labelling of datasets is time-consuming and expensive. Hence, in this work, we investigate the use of automatically-generated transcriptions of unlabelled datasets to increase the training set size. For this purpose, we use publicly-available pre-trained ASR models to automatically transcribe unlabelled datasets such as AVSpeech and VoxCeleb2. Then, we train ASR, VSR and AV-ASR models on the augmented training set, which consists of the LRS2 and LRS3 datasets as well as the additional automatically-transcribed data. We demonstrate that increasing the size of the training set, a recent trend in the literature, leads to reduced WER despite using noisy transcriptions. The proposed model achieves new state-of-the-art performance on AV-ASR on LRS2 and LRS3. In particular, it achieves a WER of 0.9% on LRS3, a relative improvement of 30% over the current state-of-the-art approach, and outperforms methods that have been trained on non-publicly available datasets with 26 times more training data.Comment: Accepted to ICASSP 202

Study on Seismic Isolation of Long Span Double Deck Steel Truss Continuous Girder Bridge

In order to improve the seismic performance of long-span double deck steel truss continuous girder bridge, taking Dao Qing Chau Bridge in Fuzhou as an engineering background, the isolation scheme of friction pendulum bearing (FPB) and friction pendulum bearing combined with viscous dampers is applied to study seismic performance. A three-dimensional dynamic model of the bridge is established using SAP2000. Taking three artificial seismic waves as seismic excitation, the seismic response of the seismic structure is calculated by nonlinear time history integration, and is then compared with the seismic response of the seismic reduction and isolation structure. The results show that the friction pendulum bearing (FPB) scheme and combined seismic dissipation and isolation (CSDI) scheme show a good seismic dissipation and isolation effect and ensure the safety of the bridge structure. However, for whole-bridge isolation, friction pendulum bearing (FPB) will produce certain residual deformations and additional stress of the bearing under the conditions of temperature and external load. For the purpose of protecting the bearing, it is recommended to use the combined seismic dissipation and isolation (CSDI) scheme

Study on Seismic Isolation of Long Span Double Deck Steel Truss Continuous Girder Bridge

In order to improve the seismic performance of long-span double deck steel truss continuous girder bridge, taking Dao Qing Chau Bridge in Fuzhou as an engineering background, the isolation scheme of friction pendulum bearing (FPB) and friction pendulum bearing combined with viscous dampers is applied to study seismic performance. A three-dimensional dynamic model of the bridge is established using SAP2000. Taking three artificial seismic waves as seismic excitation, the seismic response of the seismic structure is calculated by nonlinear time history integration, and is then compared with the seismic response of the seismic reduction and isolation structure. The results show that the friction pendulum bearing (FPB) scheme and combined seismic dissipation and isolation (CSDI) scheme show a good seismic dissipation and isolation effect and ensure the safety of the bridge structure. However, for whole-bridge isolation, friction pendulum bearing (FPB) will produce certain residual deformations and additional stress of the bearing under the conditions of temperature and external load. For the purpose of protecting the bearing, it is recommended to use the combined seismic dissipation and isolation (CSDI) scheme

Evolution of Structural and Electrical Properties of Carbon Films from Amorphous Carbon to Nanocrystalline Graphene on Quartz Glass by HFCVD

Direct growth of graphene films on glass is of great importance but has so far met with limited success. The noncatalytic property of glass results in the low decomposition ability of hydrocarbon precursors, especially at reduced temperatures (<1000 °C), and therefore amorphous carbon (a-C) films are more likely to be obtained. Here, we report the hydrogen influence on the structural and electrical properties of carbon films deposited on quartz glass at 850 °C by hot-filament chemical vapor deposition (HFCVD). The results revealed that the obtained a-C films were all graphitelike carbon films. Structural transition of the deposited films from a-C to nanocrystalline graphene was achieved by raising the hydrogen dilution ratios from 10 to over 80%. On the basis of systematic structural and chemical characterizations, a schematic process with three steps including sp² chain aggregation, aromatic ring formation, and sp³ bond etching was proposed to interpret the structural evolution. The nanocrystalline graphene films grown on glass by HFCVD exhibited good electrical performance with a carrier mobility of 36.76 cm²/(V s) and a resistivity of 5.24 × 10^–3 Ω cm over an area of 1 cm². Temperature-dependent electrical characterizations revealed that the electronic transport in carbon films was dominated by defect, localized, and extended states, respectively, when increasing the temperature from 75 to 292 K. The nanocrystalline graphene films presented higher carrier mobility and lower carrier concentration than those of a-C films, which was mainly attributed to their smaller conductive activation energy. The present investigation provides an effective way for direct growth of graphene films on glass at reduced temperatures and also offers useful insights into the understanding of structural and electrical relationship between a-C and graphene