Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization

The problem of computing category agnostic bounding box proposals is utilized as a core component in many computer vision tasks and thus has lately attracted a lot of attention. In this work we propose a new approach to tackle this problem that is based on an active strategy for generating box proposals that starts from a set of seed boxes, which are uniformly distributed on the image, and then progressively moves its attention on the promising image areas where it is more likely to discover well localized bounding box proposals. We call our approach AttractioNet and a core component of it is a CNN-based category agnostic object location refinement module that is capable of yielding accurate and robust bounding box predictions regardless of the object category. We extensively evaluate our AttractioNet approach on several image datasets (i.e. COCO, PASCAL, ImageNet detection and NYU-Depth V2 datasets) reporting on all of them state-of-the-art results that surpass the previous work in the field by a significant margin and also providing strong empirical evidence that our approach is capable to generalize to unseen categories. Furthermore, we evaluate our AttractioNet proposals in the context of the object detection task using a VGG16-Net based detector and the achieved detection performance on COCO manages to significantly surpass all other VGG16-Net based detectors while even being competitive with a heavily tuned ResNet-101 based detector. Code as well as box proposals computed for several datasets are available at:: https://github.com/gidariss/AttractioNet.Comment: Technical report. Code as well as box proposals computed for several datasets are available at:: https://github.com/gidariss/AttractioNe

LocNet: Improving Localization Accuracy for Object Detection

We propose a novel object localization methodology with the purpose of boosting the localization accuracy of state-of-the-art object detection systems. Our model, given a search region, aims at returning the bounding box of an object of interest inside this region. To accomplish its goal, it relies on assigning conditional probabilities to each row and column of this region, where these probabilities provide useful information regarding the location of the boundaries of the object inside the search region and allow the accurate inference of the object bounding box under a simple probabilistic framework. For implementing our localization model, we make use of a convolutional neural network architecture that is properly adapted for this task, called LocNet. We show experimentally that LocNet achieves a very significant improvement on the mAP for high IoU thresholds on PASCAL VOC2007 test set and that it can be very easily coupled with recent state-of-the-art object detection systems, helping them to boost their performance. Finally, we demonstrate that our detection approach can achieve high detection accuracy even when it is given as input a set of sliding windows, thus proving that it is independent of box proposal methods.Comment: Extended technical report -- short version to appear as oral paper on CVPR 2016. Code: https://github.com/gidariss/LocNet

Object detection via a multi-region & semantic segmentation-aware CNN model

We propose an object detection system that relies on a multi-region deep convolutional neural network (CNN) that also encodes semantic segmentation-aware features. The resulting CNN-based representation aims at capturing a diverse set of discriminative appearance factors and exhibits localization sensitivity that is essential for accurate object localization. We exploit the above properties of our recognition module by integrating it on an iterative localization mechanism that alternates between scoring a box proposal and refining its location with a deep CNN regression model. Thanks to the efficient use of our modules, we detect objects with very high localization accuracy. On the detection challenges of PASCAL VOC2007 and PASCAL VOC2012 we achieve mAP of 78.2% and 73.9% correspondingly, surpassing any other published work by a significant margin.Comment: Extended technical report -- short version to appear at ICCV 201

Extension of direct displacement-based design methodology for bridges to account for higher mode effects

An improvement is suggested to the direct displacement-based design (DDBD) procedure for bridges to account for higher mode effects, the key idea being not only the proper prediction of a target-displacement profile through the effective mode shape (EMS) method (wherein all significant modes are considered), but also the proper definition of the corresponding peak structural response. The proposed methodology is then applied to an actual concrete bridge wherein the different pier heights and the unrestrained transverse displacement at the abutments result in an increased contribution of the second mode. A comparison between the extended and the 'standard' DDBD is conducted, while further issues such as the proper consideration of the degree of fixity at the pier’s top and the effect of the deck’s torsional stiffness are also investigated. The proposed methodology and resulting designs are evaluated using nonlinear response-history analysis (NLRHA) for a number of spectrum-compatible motions. Unlike the 'standard' DDBD, the extended procedure adequately reproduced the target-displacement profile providing at the same time a good estimate of results regarding additional design quantities such as yield displacements, displacement ductilities etc., closely matching the results of the more rigorous NLRHA. However, the need for additional iterations clearly indicates that practical application of the proposed procedure is feasible only if it is fully 'automated', i.e. implemented in a software package

Problems associated with direct displacement-based design of concrete bridges with single-column piers, and some suggested improvements

Currently available displacement-based design (DBD) procedures for bridges are critically evaluated with a view to identifying extensions and/or modifications of the procedure, for it to be applicable to final design of a fairly broad class of bridges. An improved direct DBD procedure is presented, including a suite of comprehensive design criteria and proper consideration of the degree of fixity of the pier top. The design of an overpass bridge (originally designed to a current European Code), applying the improved ‘direct’ displacement-based design (DDBD) procedure is presented and both ‘conventional’ and displacement-based designs are assessed using non-linear response-history analysis (NLRHA); comparisons are made in terms of both economy and seismic performance of the different designs. It is seen that DDBD provided a more rational base shear distribution among piers and abutments when compared to the force-based design procedure and adequately captured the displacement pattern, closely matching the results of the more rigorous NLRHA

Surgical Treatment of Persistent Fetal Vasculature and Visual Rehabilitation: One-Year Followup

We present the management and postoperative course of a persistent fetal vasculature (PFV) case. A four-year-old girl visited the Eye Department of Hippokration, General Hospital of Thessaloniki due to reduced visual acuity of her left eye. She was diagnosed with PFV and underwent surgery (lensectomy, capsulorhexis of the posterior capsule, insertion of an intraocular lens in the posterior chamber, and posterior vitrectomy) in order to dissect the PFV. Along with the postoperative medical care, she underwent intensive treatment for amblyopia. The postoperative course was uncomplicated, and the visual acuity of her left eye improved from hand movement to 20/25 with proper correction. Patients with unilateral PFV and gradually deteriorating visual acuity could be good candidates for a combined surgical procedure, as the one described above, with a good prognosis

Optimal tuned mass-damper-inerter (TMDI) design for seismically excited MDOF structures with model uncertainties based on reliability criteria

The tuned mass-damper-inerter (TMDI) is a recently proposed linear passive dynamic vibration absorber for the seismic protection of buildings. It couples the classical tuned mass damper (TMD) with an inerter, a two-terminal device resisting the relative acceleration of its terminals, in judicial topologies, achieving mass-amplification and higher-modes-damping effects compared to the TMD. This paper considers an optimum TMDI design framework accommodating the above effects while accounting for parametric uncertainty to the host structure properties, modeled as a linear multi degree of freedom system, and to the seismic excitation, modeled as stationary colored noise. The inerter device constant, acting as a TMD mass amplifier, is treated as a design variable, whereas performance variables sensitive to high-frequency structural response dynamics are used to account for the TMDI influence to the higher structural modes. Reliability criteria are adopted for quantifying the structural performance, expressed through the probability of occurrence of different failure modes related to the trespassing of acceptable thresholds for the adopted performance variables: floor accelerations, interstory drifts, and attached mass displacement. The design objective function is taken as a linear combination of these probabilities following current performance-based seismic design trends. Analytical and simulation-based tools are adopted for the efficient estimation of the underlying stochastic integral defining the structural performance under uncertainty. A 10-story building under stationary Kanai-Tajimi stochastic excitation is considered to illustrate the design framework for various TMDI topologies and attached mass values. It is shown that the TMDI achieves enhanced structural performance and robustness to building and excitation uncertainties compared to same mass/weight TMDs

SPOT: Self-Training with Patch-Order Permutation for Object-Centric Learning with Autoregressive Transformers

Unsupervised object-centric learning aims to decompose scenes into interpretable object entities, termed slots. Slot-based auto-encoders stand out as a prominent method for this task. Within them, crucial aspects include guiding the encoder to generate object-specific slots and ensuring the decoder utilizes them during reconstruction. This work introduces two novel techniques, (i) an attention-based self-training approach, which distills superior slot-based attention masks from the decoder to the encoder, enhancing object segmentation, and (ii) an innovative patch-order permutation strategy for autoregressive transformers that strengthens the role of slot vectors in reconstruction. The effectiveness of these strategies is showcased experimentally. The combined approach significantly surpasses prior slot-based autoencoder methods in unsupervised object segmentation, especially with complex real-world images. We provide the implementation code at https://github.com/gkakogeorgiou/spot .Comment: CVPR 2024 (Highlight). Code: https://github.com/gkakogeorgiou/spo