SlimYOLOv3: Narrower, Faster and Better for Real-Time UAV Applications

Drones or general Unmanned Aerial Vehicles (UAVs), endowed with computer vision function by on-board cameras and embedded systems, have become popular in a wide range of applications. However, real-time scene parsing through object detection running on a UAV platform is very challenging, due to limited memory and computing power of embedded devices. To deal with these challenges, in this paper we propose to learn efficient deep object detectors through channel pruning of convolutional layers. To this end, we enforce channel-level sparsity of convolutional layers by imposing L1 regularization on channel scaling factors and prune less informative feature channels to obtain "slim" object detectors. Based on such approach, we present SlimYOLOv3 with fewer trainable parameters and floating point operations (FLOPs) in comparison of original YOLOv3 (Joseph Redmon et al., 2018) as a promising solution for real-time object detection on UAVs. We evaluate SlimYOLOv3 on VisDrone2018-Det benchmark dataset; compelling results are achieved by SlimYOLOv3 in comparison of unpruned counterpart, including ~90.8% decrease of FLOPs, ~92.0% decline of parameter size, running ~2 times faster and comparable detection accuracy as YOLOv3. Experimental results with different pruning ratios consistently verify that proposed SlimYOLOv3 with narrower structure are more efficient, faster and better than YOLOv3, and thus are more suitable for real-time object detection on UAVs. Our codes are made publicly available at https://github.com/PengyiZhang/SlimYOLOv3

Towards code generation from design models

With the growing in size and complexity of modern computer systems, the need for improving the quality at all stages of software development has become a critical issue. The current software production has been largely depended on manual code development. Despite the slow development process, the errors introduced by the programmers contribute to a substantial portion of defects in the final software product. This paper explores the possibility of generating code and assertion constraints from formal design models and use them to verify the implementation. We translate Z formal models into their OCL counter-parts and Java assertions. With the help of existing tools, we demonstrate various checking at different levels to enhance correctness

MetaGait: Learning to Learn an Omni Sample Adaptive Representation for Gait Recognition

Gait recognition, which aims at identifying individuals by their walking patterns, has recently drawn increasing research attention. However, gait recognition still suffers from the conflicts between the limited binary visual clues of the silhouette and numerous covariates with diverse scales, which brings challenges to the model's adaptiveness. In this paper, we address this conflict by developing a novel MetaGait that learns to learn an omni sample adaptive representation. Towards this goal, MetaGait injects meta-knowledge, which could guide the model to perceive sample-specific properties, into the calibration network of the attention mechanism to improve the adaptiveness from the omni-scale, omni-dimension, and omni-process perspectives. Specifically, we leverage the meta-knowledge across the entire process, where Meta Triple Attention and Meta Temporal Pooling are presented respectively to adaptively capture omni-scale dependency from spatial/channel/temporal dimensions simultaneously and to adaptively aggregate temporal information through integrating the merits of three complementary temporal aggregation methods. Extensive experiments demonstrate the state-of-the-art performance of the proposed MetaGait. On CASIA-B, we achieve rank-1 accuracy of 98.7%, 96.0%, and 89.3% under three conditions, respectively. On OU-MVLP, we achieve rank-1 accuracy of 92.4%.Comment: Accepted by ECCV202

PO-259 Effect of 30%，50% and 70% VO2 max treadmill exercises on gut microbiome in hypertensive mice

Objective Gut microbiome has a significant impact on human health through the interaction with host and environment, which is closely related to a series of chronic diseases. The diversity of gut microbiome and its metabolic disorder are the risk factor of hypertension. The changes of gut microbiome structure and abundance are closely related to the pathogenesis of hypertension, in which Bifidobacterium and lactic acid bacteria can bind with the hypotensive substances to show the hypotensive therapy. However, the diet and exercise have great impact on the structure and function of gut microbiome, and of which aerobic exercise could increase the ratio of gut beneficial bacteria to harmful bacteria effectively. The effect of treadmill exercise on gut microbiome of hypertensive mice was studied in this paper, which provided a theoretical basis for the prevention and treatment of hypertension by gut microbiome. Methods SPF Kunming mice were fed with 8% high salt diet for 6 weeks to make the hypertension model. Compared with the blood pressure of mice before the formal experiment, The standard was that the blood pressure of the experimental mice was increased by SBP >15% or DBP > 5%, which was indicated the model of hypertension was established successfully. The motion slope and velocity of the maximum oxygen uptake of 30%, 50%, and 70% were measured respectively. The mice were divided into 4 groups randomly according to their body weight, 10 mice/group. The 30% VO2 max, 50% VO2 max, and 70% VO2 max were exercised for 6 weeks as well as the control group. The mice were divided into cages and fed in accordance with the national standard rodent diet. The blood pressure of mice was measured weekly by tail pressure method (Tail-cuff).The abundance of Bifidobacterium, Bacteroides, Lactobacillus and Enterobacter in the gut microbiome of mice feces were tested by 16S sequencing every two weeks. The ratio of Firmicute / Bacteroides (F/B ratio) was also measured by sequencing as a parameter to reflect the disorder of gut microbiome. The ratio of Firmicutes / Bacteroides is almost equal 1, and the present study found that the F/B increased significantly in the hypertensive group. Real-time PCR was used to detect the changes of plasma inflammatory factors IL-1β, IL-6 and TNF-ɑ. The previous study had shown that the intestinal disorders can lead to an increase in pathogenic bacteria, further leading to the inflammation. Finally, the experimental data were analyzed by independent sample t-test. Results (1) After six weeks of exercise intervention, the blood pressure (132.87mm Hg±5.23mm Hg) of the exercise group was significantly lower than the control group (99.57mm Hg±7.47mm Hg), especially in the 50%VO2 max mice. (2) Compared with the rest group, the abundance of gut microbiome in the exercise group was increased, among which the number of Bifidobacteria, Lactobacillus, and Bacteroides were increased significantly, of which 50% of VO2 max group increased most significantly, and the number of Enterobacter was less than the control group (p<0.05). (3) Compared with the control group, the ratio of F/B in the exercise groups were lower than the control group, but the 50% VO2 max group was decreased most obviously (p < 0.05). (4) Compared with the control group, the plasma levels of IL-1β, IL-6 and TNF-ɑ in exercise groups were significantly lower than the control group (p < 0.05). Conclusions  (1) The aerobic exercise could change the abundance and structure of gut microbiome in hypertensive mice, increase the beneficial bacteria Bifidobacteria and lactobacillus in the intestinal tract of mice, and reduce the ratio of Firmicutes/Bacteroides, improving the gut microbiome disorder. (2) The aerobic exercise could alleviate the inflammatory reaction of the body by regulating the structure of gut microbiome of hypertensive mice, improving the blood pressure of mice; (3) 50%VO2 max was the most significant exercise intensity to improve the abundance and structure of gut microbiome in hypertensive mice. &nbsp

PO-128 Effects of 30%, 50%, 70%VO2 max treadmill exercises on gut microbiome of atherosclerotic mice

Objective  To observe the dynamic changes of gut microbiome in mice before and after different intensities of treadmill exercises, and to explore the effects of different intensities of treadmill exercises on gut microbiome of atherosclerotic (AS) mice. Methods  The 50 male ApoE-/- mice aged 8 weeks were randomly divided into 2 groups, 10 mice in the general feeding group and the other 40 mice in the AS group, which were fed with normal and high-fat diet for 4 weeks respectively. Weight and blood test were taken before and after 4 weeks feeding. The serum Triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein (HDL) levels were examined by blood test in ApoE-/- mice model, and atherosclerotic disease degeneration score of adult mice was used to determine the success of modeling after 4 weeks feeding. Then two mice selected from the normal and high-fat group respectively selected to test the maximum oxygen uptake (VO2 max) by the reformative Bedford method, and the running platform speed and slope corresponding to 30%, 50% and 70%VO2 max were determined. The 40 AS mice were randomly divided into the model blank group, the low-intensity exercise group, the medium-intensity exercise group and the high-intensity exercise group according to their weight for 4 weeks of exercises, respectively. The low-intensity exercise group was 30% VO2 max, with a slope of 10 degrees and a speed of 10 m/min. The medium intensity exercise group was 50% VO2 max with a slope of 10 degrees and a speed of 15 m/min. The high-intensity exercise group was 70% VO2 max with a slope of 10 degrees and a speed of 20 m/min. The exercises were performed for 4 weeks, 5 days a week, 20 minutes a day. The fresh feces were collected from 5 groups of mice before and after 4 weeks treadmill exercises. The number of gut Lactobacillus, Bacteroides, Firmicutes, Bifidobacterium, Verrucomicrobiaceae, Akkermansia , Escherichia coli, Collinsella and Clostridium in AS mice were analyzed by 16s sequencing.  Results 1. The TG, TC and LDL-C were significantly increased in the blood serum of the mice after the modeling, and the HDL-C was decreased, and the atherosclerotic disease degeneration score was significantly increased in the adult mice, and the modeling was successful. 2. The different intensity treadmill exercises can increase the number of gut probiotics in mice and decrease the number of harmful bacteria. The beneficial bacteria in gut tract of AS mice with moderate intensity of 50% VO2max was observed in the experiment: Bacteroides, Bifidobacterium and Verrucomicrobiaceae, Akkermansia were significantly increased, P<0.05. The harmful bacteria:Lactobacillus, Escherichia coli, Collinsella and Clostridium were significantly decreased, P<0.05. The ratio of Firmicutes / Bacteroidetes was increased. Conclusions  1. High-fat diet can lead to AS in ApoE-/- mice. 2. The different intensity treadmill exercises can reduce the weight of AS mice. 3. The number and abundance of probiotics of gut microbiome of AS mice could be promoted by different intensity treadmill exercises, among which the medium intensity aerobic exercise can significantly increase the number of beneficial bacteria such Bacteroidetes, Bifidobacteria, Verrucomicrobiaceae and Akkermansia, meanwhile, the structure of gut microbiome in AS mice was improved as well

GaitGCI: Generative Counterfactual Intervention for Gait Recognition

Gait is one of the most promising biometrics that aims to identify pedestrians from their walking patterns. However, prevailing methods are susceptible to confounders, resulting in the networks hardly focusing on the regions that reflect effective walking patterns. To address this fundamental problem in gait recognition, we propose a Generative Counterfactual Intervention framework, dubbed GaitGCI, consisting of Counterfactual Intervention Learning (CIL) and Diversity-Constrained Dynamic Convolution (DCDC). CIL eliminates the impacts of confounders by maximizing the likelihood difference between factual/counterfactual attention while DCDC adaptively generates sample-wise factual/counterfactual attention to efficiently perceive the sample-wise properties. With matrix decomposition and diversity constraint, DCDC guarantees the model to be efficient and effective. Extensive experiments indicate that proposed GaitGCI: 1) could effectively focus on the discriminative and interpretable regions that reflect gait pattern; 2) is model-agnostic and could be plugged into existing models to improve performance with nearly no extra cost; 3) efficiently achieves state-of-the-art performance on arbitrary scenarios (in-the-lab and in-the-wild).Comment: Accepted by CVPR202