ICE: Inter-instance Contrastive Encoding for Unsupervised Person Re-identification

Unsupervised person re-identification (ReID) aims at learning discriminative identity features without annotations. Recently, self-supervised contrastive learning has gained increasing attention for its effectiveness in unsupervised representation learning. The main idea of instance contrastive learning is to match a same instance in different augmented views. However, the relationship between different instances of a same identity has not been explored in previous methods, leading to sub-optimal ReID performance. To address this issue, we propose Inter-instance Contrastive Encoding (ICE) that leverages inter-instance pairwise similarity scores to boost previous class-level contrastive ReID methods. We first use pairwise similarity ranking as one-hot hard pseudo labels for hard instance contrast, which aims at reducing intra-class variance. Then, we use similarity scores as soft pseudo labels to enhance the consistency between augmented and original views, which makes our model more robust to augmentation perturbations. Experiments on several large-scale person ReID datasets validate the effectiveness of our proposed unsupervised method ICE, which is competitive with even supervised methods

Partition and Reunion: A Two-Branch Neural Network for Vehicle Re-identification

International audienceThe smart city vision raises the prospect that cities will become more intelligent in various fields, such as more sustainable environment and a better quality of life for residents. As a key component of smart cities, intelligent transportation system highlights the importance of vehicle re-identification (Re-ID). However, as compared to the rapid progress on person Re-ID, vehicle Re-ID advances at a relatively slow pace. Some previous state-of-the-art approaches strongly rely on extra annotation, like attributes (e.g., vehicle color and type) and key-points (e.g., wheels and lamps). Recent work on person Re-ID shows that extracting more local features can achieve a better performance without considering extra annotation. In this paper, we propose an end-to-end trainable two-branch Partition and Reunion Network (PRN) for the challenging vehicle Re-ID task. Utilizing only identity labels, our proposed method outperforms existing state-of-the-art methods on four vehicle Re-ID benchmark datasets, including VeRi-776, Vehi-cleID, VRIC and CityFlow-ReID by a large margin

Joint Generative and Contrastive Learning for Unsupervised Person Re-identification

Recent self-supervised contrastive learning provides an effective approach for unsupervised person re-identification (ReID) by learning invariance from different views (transformed versions) of an input. In this paper, we incorporate a Generative Adversarial Network (GAN) and a contrastive learning module into one joint training framework. While the GAN provides online data augmentation for contrastive learning, the contrastive module learns view-invariant features for generation. In this context, we propose a mesh-based view generator. Specifically, mesh projections serve as references towards generating novel views of a person. In addition, we propose a view-invariant loss to facilitate contrastive learning between original and generated views. Deviating from previous GAN-based unsupervised ReID methods involving domain adaptation, we do not rely on a labeled source dataset, which makes our method more flexible. Extensive experimental results show that our method significantly outperforms state-of-the-art methods under both, fully unsupervised and unsupervised domain adaptive settings on several large scale ReID datsets.Comment: CVPR 2021. Source code: https://github.com/chenhao2345/GC

Enhancing Diversity in Teacher-Student Networks via Asymmetric branches for Unsupervised Person Re-identification

International audienceThe objective of unsupervised person re-identification (Re-ID) is to learn discriminative features without laborintensive identity annotations. State-of-the-art unsupervised Re-ID methods assign pseudo labels to unlabeled images in the target domain and learn from these noisy pseudo labels. Recently introduced Mean Teacher Model is a promising way to mitigate the label noise. However, during the training, self-ensembled teacher-student networks quickly converge to a consensus which leads to a local minimum. We explore the possibility of using an asymmetric structure inside neural network to address this problem. First, asymmetric branches are proposed to extract features in different manners, which enhances the feature diversity in appearance signatures. Then, our proposed cross-branch supervision allows one branch to get supervision from the other branch, which transfers distinct knowledge and enhances the weight diversity between teacher and student networks. Extensive experiments show that our proposed method can significantly surpass the performance of previous work on both unsupervised domain adaptation and fully unsupervised Re-ID tasks

Partition and Reunion: A Two-Branch Neural Network for Vehicle Re-identification

International audienceThe smart city vision raises the prospect that cities will become more intelligent in various fields, such as more sustainable environment and a better quality of life for residents. As a key component of smart cities, intelligent transportation system highlights the importance of vehicle re-identification (Re-ID). However, as compared to the rapid progress on person Re-ID, vehicle Re-ID advances at a relatively slow pace. Some previous state-of-the-art approaches strongly rely on extra annotation, like attributes (e.g., vehicle color and type) and key-points (e.g., wheels and lamps). Recent work on person Re-ID shows that extracting more local features can achieve a better performance without considering extra annotation. In this paper, we propose an end-to-end trainable two-branch Partition and Reunion Network (PRN) for the challenging vehicle Re-ID task. Utilizing only identity labels, our proposed method outperforms existing state-of-the-art methods on four vehicle Re-ID benchmark datasets, including VeRi-776, Vehi-cleID, VRIC and CityFlow-ReID by a large margin

Aquatic insects dealing with dehydration: do desiccation resistance traits differ in species with contrasting habitat preferences?

Background Desiccation resistance shapes the distribution of terrestrial insects at multiple spatial scales. However, responses to drying stress have been poorly studied in aquatic groups, despite their potential role in constraining their distribution and diversification, particularly in arid and semi-arid regions. Methods We examined desiccation resistance in adults of four congeneric water beetle species (Enochrus, family Hydrophilidae) with contrasting habitat specificity (lentic vs. lotic systems and different salinity optima from fresh- to hypersaline waters). We measured survival, recovery capacity and key traits related to desiccation resistance (fresh mass, % water content, % cuticle content and water loss rate) under controlled exposure to desiccation, and explored their variability within and between species. Results Meso- and hypersaline species were more resistant to desiccation than freshwater and hyposaline ones, showing significantly lower water loss rates and higher water content. No clear patterns in desiccation resistance traits were observed between lotic and lentic species. Intraspecifically, water loss rate was positively related to specimens’ initial % water content, but not to fresh mass or % cuticle content, suggesting that the dynamic mechanism controlling water loss is mainly regulated by the amount of body water available. Discussion Our results support previous hypotheses suggesting that the evolution of desiccation resistance is associated with the colonization of saline habitats by aquatic beetles. The interespecific patterns observed in Enochrus also suggest that freshwater species may be more vulnerable than saline ones to drought intensification expected under climate change in semi-arid regions such as the Mediterranean Basin

ICE: Inter-instance Contrastive Encoding for Unsupervised Person Re-identification

International audienceUnsupervised person re-identification (ReID) aims at learning discriminative identity features without annotations. Recently, self-supervised contrastive learning has gained increasing attention for its effectiveness in unsupervised representation learning. The main idea of instance contrastive learning is to match a same instance in different augmented views. However, the relationship between different instances has not been fully explored in previous contrastive methods, especially for instance-level contrastive loss. To address this issue, we propose Interinstance Contrastive Encoding (ICE) that leverages interinstance pairwise similarity scores to boost previous classlevel contrastive ReID methods. We first use pairwise similarity ranking as one-hot hard pseudo labels for hard instance contrast, which aims at reducing intra-class variance. Then, we use similarity scores as soft pseudo labels to enhance the consistency between augmented and original views, which makes our model more robust to augmentation perturbations. Experiments on several largescale person ReID datasets validate the effectiveness of our proposed unsupervised method ICE, which is competitive with even supervised methods. Code is made available at https://github.com/chenhao2345/ICE

Learning Discriminative and Generalizable Representations by Spatial-Channel Partition for Person Re-Identification

International audienceIn Person Re-Identification (Re-ID) task, combining local and global features is a common strategy to overcome missing key parts and misalignment on models based only on global features. Using this combination, neural networks yield impressive performance in Re-ID task. Previous part-based models mainly focus on spatial partition strategies. Recently, operations on channel information, such as Group Normalization and Channel Attention, have brought significant progress to various visual tasks. However, channel partition has not drawn much attention in Person Re-ID. In this paper, we conduct a study to exploit the potential of channel partition in Re-ID task. Based on this study, we propose an end-to-end Spatial and Channel partition Representation network (SCR) in order to better exploit both spatial and channel information. Experiments conducted on three mainstream image-based evaluation protocols including Market-1501, DukeMTMC-ReID and CUHK03 and one video-based evaluation protocol MARS validate the performance of our model, which outperforms previous state-of-the-art in both single and cross domain Re-ID tasks