BRANCHING NEURAL NETWORKS

A conditional deep learning model that learns specialized representations on a decision tree is described. Unlike similar methods taking a probabilistic mixture of experts (MoE) approach, a feature augmentation based method is used to jointly train all network and decision parameters using back–propagation, which allows for deterministic binary decisions at both training and test time, specializing subtrees exclusively to clusters of data. Feature augmentation involves combining intermediate representations with scores or confidences assigned to branches. Each representation is augmented with all of the scores assigned to the active branch on the computational path to encode the entire path information, which is essential for efficient training of decision functions. These networks are referred to as Branching Neural Networks (BNNs). As this is an approach that is orthogonal to many other neural network compression methods, such algorithms can be combined to achieve much higher compression rates and further speedups

Learning to Navigate the Energy Landscape

In this paper, we present a novel and efficient architecture for addressing computer vision problems that use `Analysis by Synthesis'. Analysis by synthesis involves the minimization of the reconstruction error which is typically a non-convex function of the latent target variables. State-of-the-art methods adopt a hybrid scheme where discriminatively trained predictors like Random Forests or Convolutional Neural Networks are used to initialize local search algorithms. While these methods have been shown to produce promising results, they often get stuck in local optima. Our method goes beyond the conventional hybrid architecture by not only proposing multiple accurate initial solutions but by also defining a navigational structure over the solution space that can be used for extremely efficient gradient-free local search. We demonstrate the efficacy of our approach on the challenging problem of RGB Camera Relocalization. To make the RGB camera relocalization problem particularly challenging, we introduce a new dataset of 3D environments which are significantly larger than those found in other publicly-available datasets. Our experiments reveal that the proposed method is able to achieve state-of-the-art camera relocalization results. We also demonstrate the generalizability of our approach on Hand Pose Estimation and Image Retrieval tasks

A multimodal 3d healthcare communication system

W e p r e s e n t a system that integrates gesture recognition and 3D talking head technologies for a patient communication a p p l i c a t i o n a t a hospital or healthcare setting for supporting patients treated in bed. As a multimodal user interface, we get the input from patients using hand gestures and provide feedback by using a 3D talking avatar. Index Terms — gesture recognition, multimodal user interfaces, 3D facial animation

Fragrant grapes on the trail of flavour as part of gastronomy tourism

Anadolu, binlerce yıllık geçmişiyle üzümün anavatanı ve gen merkezi konumundadır. Bu coğrafyada yaşayan her medeniyet üzümü ve üzümden elde ettikleri ürünleri mutfak kültürlerinin içine almışlar ve mutfak kültürlerini farklılaştırmışlardır. Bu farklılık ise her geçen gün turizm açısından daha fazla çekicilik unsuru olarak değerlendirilmektedir. Bu doğrultuda değişen turizm dinamikleri ile gastronomi turistleri lezzetlerin takibini yaparak seyahatlerini planlamaktadırlar. Bu çalışma ile kokulu üzümün, Karadeniz Bölgesi’nin Orta ve Doğu sahil şeridinde yer alan illerdeki yeri ve önemi ayrıca gastronomi turizmine yönelik bir ürün olarak potansiyeli ele alınmıştır. Gastronomi turizmine yönelik ilginin arttığı sonucuna bağlı olarak, alternatif bir gastronomik ürün rotası önermesi açısından çalışma önem arz etmektedir. Çalışmanın teorik alt yapısı planlı davranış teorisine dayandırılmıştır. Çalışmanın evrenini Karadeniz Bölgesi’nin Orta ve Doğu sahil şeridinde yer alan; Samsun, Ordu, Giresun, Trabzon, Rize ve Artvin illeri oluşturmaktadır. Çalışmanın verileri nitel araştırma yöntemlerinden doküman inceleme, gözlem ve görüşme tekniğiyle toplanmıştır. Çalışmanın deseni örnek olaydır. Çalışmada veri analizi planı oluşturulmuş, betimsel analiz yöntemi ile veriler analiz edilmiştir. Çalışma ile elde edilen veriler; kokulu üzümün bölge için değerlendirilmesi gereken bir potansiyele sahip olduğu sonucunu ortaya koymaktadır. Dolayısıyla kokulu üzüme yönelik gastronomi rotalarının oluşturulması ile destinasyon çekiciliği arttırılarak destinasyon imajının gelişmesine böylece yerel halkın yaşam kalitesinin yükseltilmesine önemli katkılar sağlanabilecektirAnatolia, with its thousands of years of history, is the homeland and gene center of grapes. Every civilization living in this geography has included grapes and products produced from them into their culinary cultures and differentiated their culinary cultures. This difference is considered more and more attractive in terms of tourism day by day. With the changing tourism dynamics, gastronomy tourists plan their travels by following flavors. In this study, the place and importance of fox grape in the provinces located on the Middle and Eastern coastline of the Black Sea region, and its potential as a product for gastronomy tourism were discussed. Depending on the result that the interest in gastronomic tourism has increased, the study is important in terms of proposing an alternative route of gastronomic product. The theoretical background of the study is based on the theory of planned behavior. The population of the study consists of Samsun, Ordu, Giresun, Trabzon, Rize and Artvin provinces located in the Middle and Eastern coastline of the Black Sea region. Data of the study were collected by document analysis, observation and interview techniques, which are among the qualitative research methods. The research was designed as a case study. In the study, a data analysis plan was created and the data were analyzed using the descriptive analysis method. The data obtained from the study reveal that fox grape has a potential that should be evaluated for the region. Therefore, the creation of gastronomic routes to fox grape can contribute to increasing the attractiveness of the destination and improving the image of the destination, and thus to increasing the local people’s quality of life

Modelling and simulation studies on adaptive controller for alt-azimuth telescopes despite unknown wind disturbance and mass

Numerous ground-based observatories are using small sized ground telescopes for scientific research purposes. The telescopes that are available on the market have three main problems. These issues can be listed as: positioning repeatability, tuning requirement according to different wind speeds for different seasons, and the mass changing via different scientific equipments added to the telescope. This study is aimed at resolving these issues for ground based small alt-azimuth telescopes. Establishing of a set and forget system is performed by designing an adaptive controller. Motor dynamics are taken into consideration for a realistic mathematical model. The Wind-Gust model that consists of a sum of sinusoidal disturbances with unknown phase, amplitude and frequency is used for the wind model. The purposed controller cancels the disturbance effects on the plant while operational positioning and also the makes the plant insensitive to mass changes. The Lyapunov approach is utilised when proving the asymptotic stability. The proposed controller’s success is illustrated with thorough numerical evaluation.The authors would like to thank the technical guidance and funding support of Isik University, Center for Optomechatronics Research and Application (OPAM),and Ataturk University Center for Astrophysical Application and Research (ATASAM). The author(s) received no financial support for the research, authorship, and/or publication of this article.Publisher's Versio

In-Hand 3D Object Scanning from an RGB Sequence

We propose a method for in-hand 3D scanning of an unknown object with a monocular camera. Our method relies on a neural implicit surface representation that captures both the geometry and the appearance of the object, however, by contrast with most NeRF-based methods, we do not assume that the camera-object relative poses are known. Instead, we simultaneously optimize both the object shape and the pose trajectory. As direct optimization over all shape and pose parameters is prone to fail without coarse-level initialization, we propose an incremental approach that starts by splitting the sequence into carefully selected overlapping segments within which the optimization is likely to succeed. We reconstruct the object shape and track its poses independently within each segment, then merge all the segments before performing a global optimization. We show that our method is able to reconstruct the shape and color of both textured and challenging texture-less objects, outperforms classical methods that rely only on appearance features, and that its performance is close to recent methods that assume known camera poses.Comment: CVPR 202

AssemblyHands: Towards Egocentric Activity Understanding via 3D Hand Pose Estimation

We present AssemblyHands, a large-scale benchmark dataset with accurate 3D hand pose annotations, to facilitate the study of egocentric activities with challenging hand-object interactions. The dataset includes synchronized egocentric and exocentric images sampled from the recent Assembly101 dataset, in which participants assemble and disassemble take-apart toys. To obtain high-quality 3D hand pose annotations for the egocentric images, we develop an efficient pipeline, where we use an initial set of manual annotations to train a model to automatically annotate a much larger dataset. Our annotation model uses multi-view feature fusion and an iterative refinement scheme, and achieves an average keypoint error of 4.20 mm, which is 85% lower than the error of the original annotations in Assembly101. AssemblyHands provides 3.0M annotated images, including 490K egocentric images, making it the largest existing benchmark dataset for egocentric 3D hand pose estimation. Using this data, we develop a strong single-view baseline of 3D hand pose estimation from egocentric images. Furthermore, we design a novel action classification task to evaluate predicted 3D hand poses. Our study shows that having higher-quality hand poses directly improves the ability to recognize actions.Comment: CVPR 2023. Project page: https://assemblyhands.github.io