Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification

Many unsupervised domain adaptive (UDA) person re-identification (ReID) approaches combine clustering-based pseudo-label prediction with feature fine-tuning. However, because of domain gap, the pseudo-labels are not always reliable and there are noisy/incorrect labels. This would mislead the feature representation learning and deteriorate the performance. In this paper, we propose to estimate and exploit the credibility of the assigned pseudo-label of each sample to alleviate the influence of noisy labels, by suppressing the contribution of noisy samples. We build our baseline framework using the mean teacher method together with an additional contrastive loss. We have observed that a sample with a wrong pseudo-label through clustering in general has a weaker consistency between the output of the mean teacher model and the student model. Based on this finding, we propose to exploit the uncertainty (measured by consistency levels) to evaluate the reliability of the pseudo-label of a sample and incorporate the uncertainty to re-weight its contribution within various ReID losses, including the identity (ID) classification loss per sample, the triplet loss, and the contrastive loss. Our uncertainty-guided optimization brings significant improvement and achieves the state-of-the-art performance on benchmark datasets.Comment: 9 pages. Accepted to 35th AAAI Conference on Artificial Intelligence (AAAI 2021

Vector-based Representation is the Key: A Study on Disentanglement and Compositional Generalization

Recognizing elementary underlying concepts from observations (disentanglement) and generating novel combinations of these concepts (compositional generalization) are fundamental abilities for humans to support rapid knowledge learning and generalize to new tasks, with which the deep learning models struggle. Towards human-like intelligence, various works on disentangled representation learning have been proposed, and recently some studies on compositional generalization have been presented. However, few works study the relationship between disentanglement and compositional generalization, and the observed results are inconsistent. In this paper, we study several typical disentangled representation learning works in terms of both disentanglement and compositional generalization abilities, and we provide an important insight: vector-based representation (using a vector instead of a scalar to represent a concept) is the key to empower both good disentanglement and strong compositional generalization. This insight also resonates the neuroscience research that the brain encodes information in neuron population activity rather than individual neurons. Motivated by this observation, we further propose a method to reform the scalar-based disentanglement works ($\beta$-TCVAE and FactorVAE) to be vector-based to increase both capabilities. We investigate the impact of the dimensions of vector-based representation and one important question: whether better disentanglement indicates higher compositional generalization. In summary, our study demonstrates that it is possible to achieve both good concept recognition and novel concept composition, contributing an important step towards human-like intelligence.Comment: Preprin

EleAtt-RNN: Adding Attentiveness to Neurons in Recurrent Neural Networks

Recurrent neural networks (RNNs) are capable of modeling temporal dependencies of complex sequential data. In general, current available structures of RNNs tend to concentrate on controlling the contributions of current and previous information. However, the exploration of different importance levels of different elements within an input vector is always ignored. We propose a simple yet effective Element-wise-Attention Gate (EleAttG), which can be easily added to an RNN block (e.g. all RNN neurons in an RNN layer), to empower the RNN neurons to have attentiveness capability. For an RNN block, an EleAttG is used for adaptively modulating the input by assigning different levels of importance, i.e., attention, to each element/dimension of the input. We refer to an RNN block equipped with an EleAttG as an EleAtt-RNN block. Instead of modulating the input as a whole, the EleAttG modulates the input at fine granularity, i.e., element-wise, and the modulation is content adaptive. The proposed EleAttG, as an additional fundamental unit, is general and can be applied to any RNN structures, e.g., standard RNN, Long Short-Term Memory (LSTM), or Gated Recurrent Unit (GRU). We demonstrate the effectiveness of the proposed EleAtt-RNN by applying it to different tasks including the action recognition, from both skeleton-based data and RGB videos, gesture recognition, and sequential MNIST classification. Experiments show that adding attentiveness through EleAttGs to RNN blocks significantly improves the power of RNNs.Comment: IEEE Transactions on Image Processing (Accept). arXiv admin note: substantial text overlap with arXiv:1807.0444

First identification of long non-coding RNAs in fungal parasite Nosema ceranae

International audienceAbstractNosema ceranae is a unicellular fungal parasite of honey bees and causes huge losses for apiculture. Until present, no study on N. ceranae long non-coding RNAs (lncRNAs) was documented. Here, we sequenced purified spores of N. ceranae using strand-specific library construction and high-throughput RNA sequencing technologies. In total, 83 novel lncRNAs were predicted from N. ceranae spore samples, including lncRNAs, long intergenic non-coding RNAs (lincRNAs), and sense lncRNAs. Moreover, these lncRNAs share similar characteristics with those identified in mammals and plants, such as shorter length and fewer exon number and transcript isoforms than protein-coding genes. Finally, the expression of 12 lncRNAs was confirmed with RT-PCR, confirming their true existence. To our knowledge, this is the first evidence of lncRNAs produced by a microsporidia species, offering novel insights into basic biology such as regulation of gene expression of this widespread taxonomic group

One-step construction of ZnS/C and CdS/C one-dimensional core-shell nanostructures

ZnS/C and CdS/C coaxial core-shell nanowires, which consist of single-crystal semiconductor nanowires encapsulated by carbon nanotubes ( CNTs), have been synthesized by simple one-step thermal processes. These coaxial core-shell nanowires have uniform outer diameters ( 80-180 nm for ZnS/C, 60-130 nm for CdS/C), and uniform shell thicknesses ( ca. tens of nanometers) along their entire lengths up to tens of microns. The growth of the core-shell nanowires follows a novel metal reaction and self-catalysis vapor-liquid-solid ( VLS) mechanism. The simple method could, in principle, be extended to synthesize other metal sulfide single-crystal nanowire-filled carbon nanotubes, which can find potential applications in various fields of nanotechnology