Video Time: Properties, Encoders and Evaluation

Time-aware encoding of frame sequences in a video is a fundamental problem in video understanding. While many attempted to model time in videos, an explicit study on quantifying video time is missing. To fill this lacuna, we aim to evaluate video time explicitly. We describe three properties of video time, namely a) temporal asymmetry, b)temporal continuity and c) temporal causality. Based on each we formulate a task able to quantify the associated property. This allows assessing the effectiveness of modern video encoders, like C3D and LSTM, in their ability to model time. Our analysis provides insights about existing encoders while also leading us to propose a new video time encoder, which is better suited for the video time recognition tasks than C3D and LSTM. We believe the proposed meta-analysis can provide a reasonable baseline to assess video time encoders on equal grounds on a set of temporal-aware tasks.Comment: 14 pages, BMVC 201

Actor and Action Video Segmentation from a Sentence

This paper strives for pixel-level segmentation of actors and their actions in video content. Different from existing works, which all learn to segment from a fixed vocabulary of actor and action pairs, we infer the segmentation from a natural language input sentence. This allows to distinguish between fine-grained actors in the same super-category, identify actor and action instances, and segment pairs that are outside of the actor and action vocabulary. We propose a fully-convolutional model for pixel-level actor and action segmentation using an encoder-decoder architecture optimized for video. To show the potential of actor and action video segmentation from a sentence, we extend two popular actor and action datasets with more than 7,500 natural language descriptions. Experiments demonstrate the quality of the sentence-guided segmentations, the generalization ability of our model, and its advantage for traditional actor and action segmentation compared to the state-of-the-art.Comment: Accepted to CVPR 2018 as ora

Developing the attenuation relation for damage spectrum in X-braced steel structures with neural network

Evaluating structural damage, caused by earthquakes, is very important in seismic risk management. Zoning maps of structural damage are directly used in evaluating damage of different zones as well as planning to retrofit structures. Attenuation relation is applied in preparing the acceleration zoning of regions. Similarly, damage attenuation relations which are used in analyzing probabilistic hazard and preparing damage zoning are obtained by structural damage spectrum. This spectrum is nonlinear and designed by considering nonlinear parameters of a series of one-degree-of-freedom structures and time history dynamic analysis. After gathering and modifying 778 records of the earthquakes happened in Iran, the damage spectrum was prepared for X-braced steel structures with different specifications (yield force, hysteresis curves, and ductility capacity). Damage attenuation relation was developed for the structures through regression analysis and the obtained results were compared with those of artificial neural network method. Damage of three samples with different specifications was calculated by the developed attenuation relation. The obtained results were compared with those of time history dynamic analysis. The developed relations were used for analyzing the probabilistic damage risk and preparing the damage zoning maps for city of Qazvin, as a seismic region in Iran

Detection and estimation of damage in structures using imperialist competitive algorithm

Abstract. This paper presents a method for detection and estimation of structural damage on the basis of modal parameters of a damaged structure using imperialist competitive algorithm. The imperialist competitive algorithm was developed over the last few years in an attempt to overcome inherent limitations of traditional optimize method. In this research, imperialist competitive algorithm has been employed due to its favorable performance in detection of structural damages. The performance of the proposed method has been verified through using a benchmark problem provided by the IASC-ASCE Task Group on Structural Health Monitoring and a number of numerical examples. By way of comparison between location and amount of damage obtained from the proposed method and simulation model, it was concluded that the method is sensitive to the location and amount of damage. The results clearly revealed the superiority of the presented method in comparison with energy index method

How Can Selection of Biologically Inspired Features Improve the Performance of a Robust Object Recognition Model?

Humans can effectively and swiftly recognize objects in complex natural scenes. This outstanding ability has motivated many computational object recognition models. Most of these models try to emulate the behavior of this remarkable system. The human visual system hierarchically recognizes objects in several processing stages. Along these stages a set of features with increasing complexity is extracted by different parts of visual system. Elementary features like bars and edges are processed in earlier levels of visual pathway and as far as one goes upper in this pathway more complex features will be spotted. It is an important interrogation in the field of visual processing to see which features of an object are selected and represented by the visual cortex. To address this issue, we extended a hierarchical model, which is motivated by biology, for different object recognition tasks. In this model, a set of object parts, named patches, extracted in the intermediate stages. These object parts are used for training procedure in the model and have an important role in object recognition. These patches are selected indiscriminately from different positions of an image and this can lead to the extraction of non-discriminating patches which eventually may reduce the performance. In the proposed model we used an evolutionary algorithm approach to select a set of informative patches. Our reported results indicate that these patches are more informative than usual random patches. We demonstrate the strength of the proposed model on a range of object recognition tasks. The proposed model outperforms the original model in diverse object recognition tasks. It can be seen from the experiments that selected features are generally particular parts of target images. Our results suggest that selected features which are parts of target objects provide an efficient set for robust object recognition

Intermediate scalings in holographic RG flows and conductivities

We construct numerically finite density domain-wall solutions which interpolate between two AdS 4 fixed points and exhibit an intermediate regime of hyperscaling violation, with or without Lifshitz scaling. Such RG flows can be realized in gravitational models containing a dilatonic scalar and a massive vector field with appropriate choices of the scalar potential and couplings. The infrared AdS 4 fixed point describes a new ground state for strongly coupled quantum systems realizing such scalings, thus avoiding the well-known extensive zero temperature entropy associated with AdS2×R2. We also examine the zero temperature behavior of the optical conductivity in these backgrounds and identify two scaling regimes before the UV CFT scaling is reached. The scaling of the conductivity is controlled by the emergent IR conformal symmetry at very low frequencies, and by the intermediate scaling regime at higher frequencies

A consumer-grade LCD monitor for precise visual stimulation.

Because they were used for decades to present visual stimuli in psychophysical and psychophysiological studies, cathode ray tubes (CRTs) used to be the gold standard for stimulus presentation in vision research. Recently, as CRTs have become increasingly rare in the market, researchers have started using various types of liquid-crystal display (LCD) monitors as a replacement for CRTs. However, LCDs are typically not cost-effective when used in vision research and often cannot reach the full capacity of a high refresh rate. In this study we measured the temporal and spatial characteristics of a consumer-grade LCD, and the results suggested that a consumer-grade LCD can successfully meet all the technical demands in vision research. The tested LCD, working in a flash style like that of CRTs, demonstrated perfect consistency for initial latencies across locations, yet showed poor spatial uniformity and sluggishness in reaching the requested luminance within the first frame. After these drawbacks were addressed through software corrections, the candidate monitor showed performance comparable or superior to that of CRTs in terms of both spatial and temporal homogeneity. The proposed solution can be used as a replacement for CRTs in vision research