Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

IEEE Transactions on Pattern Analysis and Machine Intelligence

With the development of 5G and edge computing, it is increasingly important to offload intelligent media computing to edge device. Traditional media coding scheme codes the media into one binary stream without a semantic structure, which prevents many important intelligent applications from operating directly in bit-stream level, including semantic analysis, parsing specific content, media editing, etc. Therefore, in this paper, we propose a learning based Semantically Structured Coding (SSC) framework to generate Semantically Structured Bit-stream (SSB), where each part of bit-stream represents a certain object and can be directly used for aforementioned tasks. Specifically, we integrate an object detection module in our compression framework to locate and align the object in feature domain. After applying quantization and entropy coding, the features are re-organized according to detected and aligned objects to form a bit-stream. Besides, different from existing learning-based compression schemes that individually train models for specific bit-rate, we share most of model parameters among various bit-rates to significantly reduce model size for variable-rate compression. Experimental results demonstrate that only at the cost of negligible overhead, objects can be completely reconstructed from partial bit-stream. We also verified that classification and pose estimation can be directly performed on partial bit-stream without performance degradation.

Beyond Coding: Detection-driven Image Compression with Semantically Structured Bit-stream

Face restoration from low resolution and noise is important for applications of face analysis recognition. However, most existing face restoration models omit the multiple scale issues in the face restoration problem, which is still not well solved in the research area. In this paper, we propose a sequential gating ensemble network (SGEN) for a multiscale noise robust face restoration issue. To endow the network with multiscale representation ability, we first employ the principle of ensemble learning for SGEN network architecture design. The SGEN aggregates multilevel base-encoders and base-decoders into the network, which enables the network to contain multiple scales of receptive field. Instead of combining these base-en/decoders directly with nonsequential operations, the SGEN takes base-en/decoders from different levels as sequential data. Specifically, it is visualized that SGEN learns to sequentially extract high-level information from base-encoders in a bottom-up manner and restore low-level information from base-decoders in a top-down manner. Besides, we propose realizing bottom-up and top-down information combination and selection with a sequential gating unit (SGU). The SGU sequentially takes information from two different levels as inputs and decides the output based on one active input. Experimental results on the benchmark dataset demonstrate that our SGEN is more effective at multiscale human face restoration with more image details and less noise than state-of-the-art image restoration models. Further utilizing an adversarial training scheme, SGEN also produces more visually preferred results than other models under subjective evaluation.

Sequential Gating Ensemble Network for Noise Robust Multiscale Face Restoration

An algorithm for efficiently compressing floating-point data in 3D meshes is disclosed. 3D meshes are represented by topology data, geometry data and property data. Geometry data specify vertex locations and are usually represented by floating-point coordinates. While geometry data are usually compressed by quantization, prediction and entropy coding, the present invention uses no prediction. A floating-point number consists of mantissa and exponent, and normally the exponent, sign and mantissa are compressed separately. A method for encoding floating-point formatted data comprises determining if a current floating-point value was previously stored in a memory, storing the current value in the memory if it was not previously stored in the memory, and encoding it. Otherwise, if the current floating-point value was previously stored in a memory, the storage position of the value within the memory is determined and a reference pointing to the storage position is encoded.

Method for encoding floating-point data, method for decoding floating-point data, and corresponding encoder and decoder

Person re-identification (reID) aims to match person images to retrieve the ones with the same identity. This is a challenging task, as the images to be matched are generally semantically misaligned due to the diversity of human poses and capture viewpoints, incompleteness of the visible bodies (due to occlusion), etc. In this paper, we propose a framework that drives the reID network to learn semantics-aligned feature representation through delicate supervision designs. Specifically, we build a Semantics Aligning Network (SAN) which consists of a base network as encoder (SA-Enc) for re-ID, and a decoder (SA-Dec) for reconstructing/regressing the densely semantics aligned full texture image. We jointly train the SAN under the supervisions of person re-identification and aligned texture generation. Moreover, at the decoder, besides the reconstruction loss, we add Triplet ReID constraints over the feature maps as the perceptual losses. The decoder is discarded in the inference and thus our scheme is computationally efficient. Ablation studies demonstrate the effectiveness of our design. We achieve the state-of-the-art performances on the benchmark datasets CUHK03, Market1501, MSMT17, and the partial person reID dataset Partial REID. Code for our proposed method is available at: this https URL.

Semantics-Aligned Representation Learning for Person Re-identification

In this paper, we present an end-to-end video compression network for P-frame challenge on CLIC. We focus on deep neural network (DNN) based video compression, and improve the current frameworks from three aspects. First, we notice that pixel space residuals is sensitive to the prediction errors of optical flow based motion compensation. To suppress the relative influence, we propose to compress the residuals of image feature rather than the residuals of image pixels. Furthermore, we combine the advantages of both pixel-level and feature-level residual compression methods by model ensembling. Finally, we propose a step-by-step training strategy to improve the training efficiency of the whole framework. Experiment results indicate that our proposed method achieves 0.9968 MS-SSIM on CLIC validation set and 0.9967 MS-SSIM on test set.

/pdf/learned-video-compression-with-feature-level-residuals-1n7g1u8qmg.pdf

Zhibo Chen

Papers

Beyond Coding: Detection-driven Image Compression with Semantically Structured Bit-stream

Sequential Gating Ensemble Network for Noise Robust Multiscale Face Restoration

Method for encoding floating-point data, method for decoding floating-point data, and corresponding encoder and decoder

Semantics-Aligned Representation Learning for Person Re-identification

Learned Video Compression with Feature-level Residuals.