OpenNet: Incremental Learning for Autonomous Driving Object Detection with Balanced Loss

Automated driving object detection has always been a challenging task in computer vision due to environmental uncertainties. These uncertainties include significant differences in object sizes and encountering the class unseen. It may result in poor performance when traditional object detection models are directly applied to automated driving detection. Because they usually presume fixed categories of common traffic participants, such as pedestrians and cars. Worsely, the huge class imbalance between common and novel classes further exacerbates performance degradation. To address the issues stated, we propose OpenNet to moderate the class imbalance with the Balanced Loss, which is based on Cross Entropy Loss. Besides, we adopt an inductive layer based on gradient reshaping to fast learn new classes with limited samples during incremental learning. To against catastrophic forgetting, we employ normalized feature distillation. By the way, we improve multi-scale detection robustness and unknown class recognition through FPN and energy-based detection, respectively. The Experimental results upon the CODA dataset show that the proposed method can obtain better performance than that of the existing methods

When Source-Free Domain Adaptation Meets Label Propagation

Source-free domain adaptation, where only a pre-trained source model is used to adapt to the target distribution, is a more general approach to achieving domain adaptation. However, it can be challenging to capture the inherent structure of the target features accurately due to the lack of supervised information on the target domain. To tackle this problem, we propose a novel approach called Adaptive Local Transfer (ALT) that tries to achieve efficient feature clustering from the perspective of label propagation. ALT divides the target data into inner and outlier samples based on the adaptive threshold of the learning state, and applies a customized learning strategy to best fits the data property. Specifically, inner samples are utilized for learning intra-class structure thanks to their relatively well-clustered properties. The low-density outlier samples are regularized by input consistency to achieve high accuracy with respect to the ground truth labels. In this way, local clustering can be prevented from forming spurious clusters while effectively propagating label information among subpopulations. Empirical evidence demonstrates that ALT outperforms the state of the arts on three public benchmarks: Office-31, Office-Home, and VisDA

Invariants of the Space Point Element Structure and Their Applications

In this paper, a new geometric structure of projective invariants is proposed. Compared with the traditional invariant calculation method based on 3D reconstruction, this method is comparable in the reliability of invariant calculation. According to this method, the only thing needed to find out is the geometric relationship between 3D points and 2D points, and the invariant can be obtained by using a single frame image. In the method based on 3D reconstruction, the basic matrix of two images is estimated first, and then, the 3D projective invariants are calculated according to the basic matrix. Therefore, in terms of algorithm complexity, the method proposed in this paper is superior to the traditional method. In this paper, we also study the projection transformation from a 3D point to a 2D point in space. According to this relationship, the geometric invariant relationships of other point structures can be easily derived, which have important applications in model-based object recognition. At the same time, the experimental results show that the eight-point structure invariants proposed in this paper can effectively describe the essential characteristics of the 3D structure of the target, without the influence of view, scaling, lighting, and other link factors, and have good stability and reliability

Tongue Images Classification Based on Constrained High Dispersal Network

Computer aided tongue diagnosis has a great potential to play important roles in traditional Chinese medicine (TCM). However, the majority of the existing tongue image analyses and classification methods are based on the low-level features, which may not provide a holistic view of the tongue. Inspired by deep convolutional neural network (CNN), we propose a novel feature extraction framework called constrained high dispersal neural networks (CHDNet) to extract unbiased features and reduce human labor for tongue diagnosis in TCM. Previous CNN models have mostly focused on learning convolutional filters and adapting weights between them, but these models have two major issues: redundancy and insufficient capability in handling unbalanced sample distribution. We introduce high dispersal and local response normalization operation to address the issue of redundancy. We also add multiscale feature analysis to avoid the problem of sensitivity to deformation. Our proposed CHDNet learns high-level features and provides more classification information during training time, which may result in higher accuracy when predicting testing samples. We tested the proposed method on a set of 267 gastritis patients and a control group of 48 healthy volunteers. Test results show that CHDNet is a promising method in tongue image classification for the TCM study

No rate heterogeneity between nucleosomal DNA and linker regions.

<p>There is no significant difference in substitution (A) and indel (B) rate between linkers and nucleosomal DNA. All: all nucleosomes, plus1: only +1 nucleosome; plus2345: +2∼+5 nucleosomes representing genic regions; intergenic: nucleosomes in intergenic regions. However, the rates in intergenic nucleosomes are much higher than those in genic regions. The vertical dotted lines indicate the border between linkers and nucleosomes.</p

Inverse association between sequence divergence rates and nucleosome occupancy around splicing sites.

<p>The left panel shows genetic variation rates (blue: substitution, red: indel) and H2A.Z nucleosome occupancy (gray area) around acceptor sites. The right panel shows the case around donor sites. H2A.Z is enriched within exons with a well-positioned nucleosome at both borders, and largely depleted inside introns. Contrary to this, sequence variation rates are very low within exons and precipitously increase inside introns.</p

Negative correlation between sequence divergence rates and nucleosome occupancy around gene ends.

<p>The left panel shows the distribution of substitution rate (blue), indel rate (red), and H2A.Z nucleosome occupancy (gray area) around gene start site. H2A.Z is enriched in coding region by a canonical +1, +2, etc. uniform nucleosome organization, and depleted in the promoter region. The top mega gene indicates positioning of the first five nucleosomes downstream to TSS. In contrast, both substitution and indel rates are much higher in the promoter region than in coding regions, and peak at ∼200 bp upstream of TSS. The right panel shows the situation around gene end site. H2A.Z occupancy is lower in gene end site than in gene start site, and lacks a uniform positioning. A ∼200-bp nucleosome depleted region centers at TTS. Substitution and indel rates are low at gene end, but precipitously increase immediately after TTS.</p