Evaluating Two-Stream CNN for Video Classification

Videos contain very rich semantic information. Traditional hand-crafted features are known to be inadequate in analyzing complex video semantics. Inspired by the huge success of the deep learning methods in analyzing image, audio and text data, significant efforts are recently being devoted to the design of deep nets for video analytics. Among the many practical needs, classifying videos (or video clips) based on their major semantic categories (e.g., "skiing") is useful in many applications. In this paper, we conduct an in-depth study to investigate important implementation options that may affect the performance of deep nets on video classification. Our evaluations are conducted on top of a recent two-stream convolutional neural network (CNN) pipeline, which uses both static frames and motion optical flows, and has demonstrated competitive performance against the state-of-the-art methods. In order to gain insights and to arrive at a practical guideline, many important options are studied, including network architectures, model fusion, learning parameters and the final prediction methods. Based on the evaluations, very competitive results are attained on two popular video classification benchmarks. We hope that the discussions and conclusions from this work can help researchers in related fields to quickly set up a good basis for further investigations along this very promising direction.Comment: ACM ICMR'1

Improving Small Object Proposals for Company Logo Detection

Many modern approaches for object detection are two-staged pipelines. The first stage identifies regions of interest which are then classified in the second stage. Faster R-CNN is such an approach for object detection which combines both stages into a single pipeline. In this paper we apply Faster R-CNN to the task of company logo detection. Motivated by its weak performance on small object instances, we examine in detail both the proposal and the classification stage with respect to a wide range of object sizes. We investigate the influence of feature map resolution on the performance of those stages. Based on theoretical considerations, we introduce an improved scheme for generating anchor proposals and propose a modification to Faster R-CNN which leverages higher-resolution feature maps for small objects. We evaluate our approach on the FlickrLogos dataset improving the RPN performance from 0.52 to 0.71 (MABO) and the detection performance from 0.52 to 0.67 (mAP).Comment: 8 Pages, ICMR 201

Investigating Grain Separation and Cleaning Efficiency Distribution of a Conventional Stationary Rasp-bar Sorghum Thresher

A stationary grain thresher was developed and used to study grain separation and cleaning efficiency distribution of the cleaning unit, fractionated by sieve and horizontal air stream, along the sieve length. The influence of feed rate, m, air speed, VA and sieve oscillation frequency, FS on cleaning efficiency of sorghum was explored. Grain separation along the sieve can be divided into three sections: increasing, peak and decreasing sections. Results showed that cleaning efficiency decreased with increasing sieve oscillations frequency and feed rate respectively. Cleaning loss increased with increasing sieve oscillation frequency, feed rate and air speed

Efficient On-the-fly Category Retrieval using ConvNets and GPUs

We investigate the gains in precision and speed, that can be obtained by using Convolutional Networks (ConvNets) for on-the-fly retrieval - where classifiers are learnt at run time for a textual query from downloaded images, and used to rank large image or video datasets. We make three contributions: (i) we present an evaluation of state-of-the-art image representations for object category retrieval over standard benchmark datasets containing 1M+ images; (ii) we show that ConvNets can be used to obtain features which are incredibly performant, and yet much lower dimensional than previous state-of-the-art image representations, and that their dimensionality can be reduced further without loss in performance by compression using product quantization or binarization. Consequently, features with the state-of-the-art performance on large-scale datasets of millions of images can fit in the memory of even a commodity GPU card; (iii) we show that an SVM classifier can be learnt within a ConvNet framework on a GPU in parallel with downloading the new training images, allowing for a continuous refinement of the model as more images become available, and simultaneous training and ranking. The outcome is an on-the-fly system that significantly outperforms its predecessors in terms of: precision of retrieval, memory requirements, and speed, facilitating accurate on-the-fly learning and ranking in under a second on a single GPU.Comment: Published in proceedings of ACCV 201

Assessment of Workers’ Level of Exposure to Work-Related Musculoskeletal Discomfort in Dewatered Cassava Mash Sieving Process

This study was undertaken to assess the level of exposure of processors to work-related musculoskeletal disorder when using the locally developed traditional sieve in the sieving process. Quick ergonomic checklist (QEC)  involving the researcher’s and the processors’ assessment using the risk assessment checklist, was used in this assessment and data was obtained from a sample of one hundred and eight (108) processors randomly selected from three senatorial districts of Rivers State. Thirty-six processors from each zone comprising of 14 males and 22 females, were selected., and assessed on the bases of their back, shoulder/arm, wrist/hand and neck posture and frequency of movement during traditional sieving process. The result of the assessment showed that the highest risk of discomfort occurred at the region of the wrist/hand, followed by back, shoulder/arm, and neck. The posture used in the sieving process exposed the processors, not only to the discomfort of pain but also put them at high risk of musculoskeletal disorder at indicated by  a high level of percentage exposure of 66% QEC rating. The result indicated a need for immediate attention and change to an improved method that will reduce the discomfort on the body parts assessed. identified parts

Single Shot Temporal Action Detection

Temporal action detection is a very important yet challenging problem, since videos in real applications are usually long, untrimmed and contain multiple action instances. This problem requires not only recognizing action categories but also detecting start time and end time of each action instance. Many state-of-the-art methods adopt the "detection by classification" framework: first do proposal, and then classify proposals. The main drawback of this framework is that the boundaries of action instance proposals have been fixed during the classification step. To address this issue, we propose a novel Single Shot Action Detector (SSAD) network based on 1D temporal convolutional layers to skip the proposal generation step via directly detecting action instances in untrimmed video. On pursuit of designing a particular SSAD network that can work effectively for temporal action detection, we empirically search for the best network architecture of SSAD due to lacking existing models that can be directly adopted. Moreover, we investigate into input feature types and fusion strategies to further improve detection accuracy. We conduct extensive experiments on two challenging datasets: THUMOS 2014 and MEXaction2. When setting Intersection-over-Union threshold to 0.5 during evaluation, SSAD significantly outperforms other state-of-the-art systems by increasing mAP from 19.0% to 24.6% on THUMOS 2014 and from 7.4% to 11.0% on MEXaction2.Comment: ACM Multimedia 201

DRYING CHARACTERISTICS, COLOUR AND PHYTOCHEMICAL CONSTITUENTS OF BRYOPHYLLUM PINNATUM LEAVE: A FUNCTION OF PRETREATMENT AND DRYING METHODS.

The impact of three different drying methods (active solar, direct open sun and hot-air cabinet tray drying) and two different treatment methods (perforated and sliced) on drying characteristics, phytochemical constituents and colour of Bryophyllumpinnatum leaves were investigated. Moisture content of the leaves was measured until equilibrium moisture content is reached. The result obtained revealed that hot air cabinet drying method and slicing pretreatment improves the drying rate of Bryophyllumpinnatum leaves more than active solar and open sun drying methods. Active solar drying method and perforated pretreatment had the best Bryophyllumpinnatum leaves phytochemical constituents and colour retention during drying with brightness L*value (39±0.11), greenness a* (-70±0.60), yellowness b* (82±0.31), colour change ΔE* and intensity C (131.61±0.12) and(107.81±0.90). Logarithmic model had the best potential for describing the drying characteristics of both treated and untreated Bryophylliumpinnatum leaves using active solar and cabinet drying method

The age of data-driven proteomics : how machine learning enables novel workflows

A lot of energy in the field of proteomics is dedicated to the application of challenging experimental workflows, which include metaproteomics, proteogenomics, data independent acquisition (DIA), non-specific proteolysis, immunopeptidomics, and open modification searches. These workflows are all challenging because of ambiguity in the identification stage; they either expand the search space and thus increase the ambiguity of identifications, or, in the case of DIA, they generate data that is inherently more ambiguous. In this context, machine learning-based predictive models are now generating considerable excitement in the field of proteomics because these predictive models hold great potential to drastically reduce the ambiguity in the identification process of the above-mentioned workflows. Indeed, the field has already produced classical machine learning and deep learning models to predict almost every aspect of a liquid chromatography-mass spectrometry (LC-MS) experiment. Yet despite all the excitement, thorough integration of predictive models in these challenging LC-MS workflows is still limited, and further improvements to the modeling and validation procedures can still be made. In this viewpoint we therefore point out highly promising recent machine learning developments in proteomics, alongside some of the remaining challenges

Towards Bottom-Up Analysis of Social Food

in ACM Digital Health Conference 201

An early resource characterization of deep learning on wearables, smartphones and internet-of-things devices

Detecting and reacting to user behavior and ambient context are core elements of many emerging mobile sensing and Internet-of-Things (IoT) applications. However, extracting accurate infer-ences from raw sensor data is challenging within the noisy and complex environments where these systems are deployed. Deep Learning { is one of the most promising approaches for overcom-ing this challenge, and achieving more robust and reliable infer-ence. Techniques developed within this rapidly evolving area of machine learning are now state-of-the-art for many inference tasks (such as, audio sensing and computer vision) commonly needed by IoT and wearable applications. But currently deep learning al-gorithms are seldom used in mobile/IoT class hardware because they often impose debilitating levels of system overhead (e.g., memory, computation and energy). Efforts to address this bar-rier to deep learning adoption are slowed by our lack of a system-atic understanding of how these algorithms behave at inference time on resource constrained hardware. In this paper, we present the-rst { albeit preliminary { measurement study of common deep learning models (such as Convolutional Neural Networks and Deep Neural Networks) on representative mobile and embed-ded platforms. The aim of this investigation is to begin to build knowledge of the performance characteristics, resource require-ments and the execution bottlenecks for deep learning models when being used to recognize categories of behavior and context. The results and insights of this study, lay an empirical foundation for the development of optimization methods and execution envi-ronments that enable deep learning to be more readily integrated into next-generation IoT, smartphones and wearable systems