The Application of Two-level Attention Models in Deep Convolutional Neural Network for Fine-grained Image Classification

Fine-grained classification is challenging because categories can only be discriminated by subtle and local differences. Variances in the pose, scale or rotation usually make the problem more difficult. Most fine-grained classification systems follow the pipeline of finding foreground object or object parts (where) to extract discriminative features (what). In this paper, we propose to apply visual attention to fine-grained classification task using deep neural network. Our pipeline integrates three types of attention: the bottom-up attention that propose candidate patches, the object-level top-down attention that selects relevant patches to a certain object, and the part-level top-down attention that localizes discriminative parts. We combine these attentions to train domain-specific deep nets, then use it to improve both the what and where aspects. Importantly, we avoid using expensive annotations like bounding box or part information from end-to-end. The weak supervision constraint makes our work easier to generalize. We have verified the effectiveness of the method on the subsets of ILSVRC2012 dataset and CUB200_2011 dataset. Our pipeline delivered significant improvements and achieved the best accuracy under the weakest supervision condition. The performance is competitive against other methods that rely on additional annotations

Spectral Unsupervised Domain Adaptation for Visual Recognition

Unsupervised domain adaptation (UDA) aims to learn a well-performed model in an unlabeled target domain by leveraging labeled data from one or multiple related source domains. It remains a great challenge due to 1) the lack of annotations in the target domain and 2) the rich discrepancy between the distributions of source and target data. We propose Spectral UDA (SUDA), an efficient yet effective UDA technique that works in the spectral space and is generic across different visual recognition tasks in detection, classification and segmentation. SUDA addresses UDA challenges from two perspectives. First, it mitigates inter-domain discrepancies by a spectrum transformer (ST) that maps source and target images into spectral space and learns to enhance domain-invariant spectra while suppressing domain-variant spectra simultaneously. To this end, we design novel adversarial multi-head spectrum attention that leverages contextual information to identify domain-variant and domain-invariant spectra effectively. Second, it mitigates the lack of annotations in target domain by introducing multi-view spectral learning which aims to learn comprehensive yet confident target representations by maximizing the mutual information among multiple ST augmentations capturing different spectral views of each target sample. Extensive experiments over different visual tasks (e.g., detection, classification and segmentation) show that SUDA achieves superior accuracy and it is also complementary with state-of-the-art UDA methods with consistent performance boosts but little extra computation

Attentional Neural Network: Feature Selection Using Cognitive Feedback

Attentional Neural Network is a new framework that integrates top-down cognitive bias and bottom-up feature extraction in one coherent architecture. The top-down influence is especially effective when dealing with high noise or difficult segmentation problems. Our system is modular and extensible. It is also easy to train and cheap to run, and yet can accommodate complex behaviors. We obtain classification accuracy better than or competitive with state of art results on the MNIST variation dataset, and successfully disentangle overlaid digits with high success rates. We view such a general purpose framework as an essential foundation for a larger system emulating the cognitive abilities of the whole brain.Comment: Poster in Neural Information Processing Systems (NIPS) 201

Facile Preparation of Bimetallic MOF-derived Supported Tungstophosphoric Acid Composites for Biodiesel Production

In this work, the novel TPA@C-NiZr-MOF catalyst is synthesized by the impregnation of tungstophosphoric acid (TPA) on the NiZr-based metal-organic framework (NiZr-MOF) followed by calcination up to 300 °C. The as-prepared catalyst materials were structurally, morphologically, and texturally characterized by XRD, FTIR, temperature programmed desorption of NH3 ( TPD-NH3 ), N2 physisorption, SEM, TEM, and XPS. The prepared catalyst can be used as an efficient heterogeneous catalyst for biodiesel production from oleic acid (OA) with methanol. The results indicated that, in comparison to TPA@NiZr-MOF, the TPA@C-NiZr-MOF catalyst calcined at 300 °C exhibits excellent catalytic performance probably owing to the synergistic effect between TPA and metal oxide skeletons, high acidity, as well as larger surface area and pore size. Additionally, the TPA@C-NiZr-MOF catalyst can be reused in up to six cycles with an acceptable conversion. This study showed that the bimetallic MOF-derived composite materials can be used as an alternative potential heterogeneous catalyst toward biorefinery applications

The Validity of CET-6 among Chinese Students Studying Overseas

This paper focuses on the validity of College English Test Band 6 (CET-6) in oversea life among Chinese students to find out whether the scores of CET-6 can truly reflect students’ English language ability and whether it is possible to use the scores of CET-6 as a proof for English language proficiency. To do the survey, we conducted the survey by quantitative research methods with 50 samples in Universiti Putra Malaysia(UPM). After the collection and analysis of data, some current issues about the assessment standards of CET-6 are found, and suggestions are also given to improve the validity of CET-6

Scaling Analysis of the Tensile Strength of Bamboo Fibers Using Weibull Statistics

This study demonstrates the effect of weak-link scaling on the tensile strength of bamboo fibers. The proposed model considers the random nature of fiber strength, which is reflected by using a two-parameter Weibull distribution function. Tension tests were performed on samples that could be scaled in length. The size effects in fiber length on the strength were analyzed based on Weibull statistics. The results verify the use of Weibull parameters from specimen testing for predicting the strength distributions of fibers of longer gauge lengths