Efficient Layer Compression Without Pruning.

Network pruning is one of the chief means for improving the computational efficiency of Deep Neural Networks (DNNs). Pruning-based methods generally discard network kernels, channels, or layers, which however inevitably will disrupt original well-learned network correlation and thus lead to performance degeneration. In this work, we propose an Efficient Layer Compression (ELC) approach to efficiently compress serial layers by decoupling and merging rather than pruning. Specifically, we first propose a novel decoupling module to decouple the layers, enabling us readily merge serial layers that include both nonlinear and convolutional layers. Then, the decoupled network is losslessly merged based on the equivalent conversion of the parameters. In this way, our ELC can effectively reduce the depth of the network without destroying the correlation of the convolutional layers. To our best knowledge, we are the first to exploit the mergeability of serial convolutional layers for lossless network layer compression. Experimental results conducted on two datasets demonstrate that our method retains superior performance with a FLOPs reduction of 74.1% for VGG-16 and 54.6% for ResNet-56, respectively. In addition, our ELC improves the inference speed by 2× on Jetson AGX Xavier edge device.

A Memorizing and Generalizing Framework for Lifelong Person Re-Identification.

In this paper, we introduce a challenging yet practical setting for person re-identification (ReID) task, named lifelong person re-identification (LReID), which aims to continuously train a ReID model across multiple domains and the trained model is required to generalize well on both seen and unseen domains. It is therefore critical to learn a ReID model that can learn a generalized representation without forgetting knowledge of seen domains. In this paper, we propose a new MEmorizing and GEneralizing framework (MEGE) for LReID, which can jointly prevent the model from forgetting and improve its generalization ability. Specifically, our MEGE is composed of two novel modules, i.e., Adaptive Knowledge Accumulation (AKA) and differentiable Ranking Consistency Distillation (RCD). Taking inspiration from the cognitive processes in the human brain, we endow AKA with two special capacities, knowledge representation and knowledge operation by graph convolution networks. AKA can effectively mitigate catastrophic forgetting on seen domains while improving the generalization ability to unseen domains. By considering the ranking factor that is specifically important in ReID, RCD is designed to distill the ranking knowledge in a differentiable manner, which can further prevent the catastrophic forgetting. To supporting the study of LReID, we build a new and large-scale benchmark with two practical evaluation protocols that consider the metrics of non-forgetting and generalization. Experiments demonstrate that 1) our MEGE framework can effectively improve the performance on seen and unseen domains under the domain-incremental learning constraint, and that 2) the proposed MEGE outperforms state-of-the-art competitors by large margins.

Win-Win by Competition: Auxiliary-Free Cloth-Changing Person Re-Identification.

Recent person Re-IDentification (ReID) systems have been challenged by changes in personnel clothing, leading to the study of Cloth-Changing person ReID (CC-ReID). Commonly used techniques involve incorporating auxiliary information (e.g., body masks, gait, skeleton, and keypoints) to accurately identify the target pedestrian. However, the effectiveness of these methods heavily relies on the quality of auxiliary information and comes at the cost of additional computational resources, ultimately increasing system complexity. This paper focuses on achieving CC-ReID by effectively leveraging the information concealed within the image. To this end, we introduce an Auxiliary-free Competitive IDentification (ACID) model. It achieves a win-win situation by enriching the identity (ID)-preserving information conveyed by the appearance and structure features while maintaining holistic efficiency. In detail, we build a hierarchical competitive strategy that progressively accumulates meticulous ID cues with discriminating feature extraction at the global, channel, and pixel levels during model inference. After mining the hierarchical discriminative clues for appearance and structure features, these enhanced ID-relevant features are crosswise integrated to reconstruct images for reducing intra-class variations. Finally, by combing with self- and cross-ID penalties, the ACID is trained under a generative adversarial learning framework to effectively minimize the distribution discrepancy between the generated data and real-world data. Experimental results on four public cloth-changing datasets (i.e., PRCC-ReID, VC-Cloth, LTCC-ReID, and Celeb-ReID) demonstrate the proposed ACID can achieve superior performance over state-of-the-art methods. The code is available soon at: https://github.com/BoomShakaY/Win-CCReID.

Querying Labeled for Unlabeled: Cross-Image Semantic Consistency Guided Semi-Supervised Semantic Segmentation.

Semi-supervised semantic segmentation aims to learn a semantic segmentation model via limited labeled images and adequate unlabeled images. The key to this task is generating reliable pseudo labels for unlabeled images. Existing methods mainly focus on producing reliable pseudo labels based on the confidence scores of unlabeled images while largely ignoring the use of labeled images with accurate annotations. In this paper, we propose a Cross-Image Semantic Consistency guided Rectifying (CISC-R) approach for semi-supervised semantic segmentation, which explicitly leverages the labeled images to rectify the generated pseudo labels. Our CISC-R is inspired by the fact that images belonging to the same class have a high pixel-level correspondence. Specifically, given an unlabeled image and its initial pseudo labels, we first query a guiding labeled image that shares the same semantic information with the unlabeled image. Then, we estimate the pixel-level similarity between the unlabeled image and the queried labeled image to form a CISC map, which guides us to achieve a reliable pixel-level rectification for the pseudo labels. Extensive experiments on the PASCAL VOC 2012, Cityscapes, and COCO datasets demonstrate that the proposed CISC-R can significantly improve the quality of the pseudo labels and outperform the state-of-the-art methods. Code is available at https://github.com/Luffy03/CISC-R.

Towards Robust Person Re-Identification by Defending Against Universal Attackers.

Recent studies show that deep person re-identification (re-ID) models are vulnerable to adversarial examples, so it is critical to improving the robustness of re-ID models against attacks. To achieve this goal, we explore the strengths and weaknesses of existing re-ID models, i.e., designing learning-based attacks and training robust models by defending against the learned attacks. The contributions of this paper are three-fold: First, we build a holistic attack-defense framework to study the relationship between the attack and defense for person re-ID. Second, we introduce a combinatorial adversarial attack that is adaptive to unseen domains and unseen model types. It consists of distortions in pixel and color space (i.e., mimicking camera shifts). Third, we propose a novel virtual-guided meta-learning algorithm for our attack-defense system. We leverage a virtual dataset to conduct experiments under our meta-learning framework, which can explore the cross-domain constraints for enhancing the generalization of the attack and the robustness of the re-ID model. Comprehensive experiments on three large-scale re-ID benchmarks demonstrate that: 1) Our combinatorial attack is effective and highly universal in cross-model and cross-dataset scenarios; 2) Our meta-learning algorithm can be readily applied to different attack and defense approaches, which can reach consistent improvement; 3) The defense model trained on the learning-to-learn framework is robust to recent SOTA attacks that are not even used during training.

Joint Representation Learning and Keypoint Detection for Cross-View Geo-Localization.

In this paper, we study the cross-view geo-localization problem to match images from different viewpoints. The key motivation underpinning this task is to learn a discriminative viewpoint-invariant visual representation. Inspired by the human visual system for mining local patterns, we propose a new framework called RK-Net to jointly learn the discriminative Representation and detect salient Keypoints with a single Network. Specifically, we introduce a Unit Subtraction Attention Module (USAM) that can automatically discover representative keypoints from feature maps and draw attention to the salient regions. USAM contains very few learning parameters but yields significant performance improvement and can be easily plugged into different networks. We demonstrate through extensive experiments that (1) by incorporating USAM, RK-Net facilitates end-to-end joint learning without the prerequisite of extra annotations. Representation learning and keypoint detection are two highly-related tasks. Representation learning aids keypoint detection. Keypoint detection, in turn, enriches the model capability against large appearance changes caused by viewpoint variants. (2) USAM is easy to implement and can be integrated with existing methods, further improving the state-of-the-art performance. We achieve competitive geo-localization accuracy on three challenging datasets, i. e., University-1652, CVUSA and CVACT. Our code is available at https://github.com/AggMan96/RK-Net.

Structure-Guided Cross-Attention Network for Cross-Domain OCT Fluid Segmentation.

Accurate retinal fluid segmentation on Optical Coherence Tomography (OCT) images plays an important role in diagnosing and treating various eye diseases. The art deep models have shown promising performance on OCT image segmentation given pixel-wise annotated training data. However, the learned model will achieve poor performance on OCT images that are obtained from different devices (domains) due to the domain shift issue. This problem largely limits the real-world application of OCT image segmentation since the types of devices usually are different in each hospital. In this paper, we study the task of cross-domain OCT fluid segmentation, where we are given a labeled dataset of the source device (domain) and an unlabeled dataset of the target device (domain). The goal is to learn a model that can perform well on the target domain. To solve this problem, in this paper, we propose a novel Structure-guided Cross-Attention Network (SCAN), which leverages the retinal layer structure to facilitate domain alignment. Our SCAN is inspired by the fact that the retinal layer structure is robust to domains and can reflect regions that are important to fluid segmentation. In light of this, we build our SCAN in a multi-task manner by jointly learning the retinal structure prediction and fluid segmentation. To exploit the mutual benefit between layer structure and fluid segmentation, we further introduce a cross-attention module to measure the correlation between the layer-specific feature and the fluid-specific feature encouraging the model to concentrate on highly relative regions during domain alignment. Moreover, an adaptation difficulty map is evaluated based on the retinal structure predictions from different domains, which enforces the model focus on hard regions during structure-aware adversarial learning. Extensive experiments on the three domains of the RETOUCH dataset demonstrate the effectiveness of the proposed method and show that our approach produces state-of-the-art performance on cross-domain OCT fluid segmentation.

Learning to Adapt Invariance in Memory for Person Re-Identification.

This work considers the problem of unsupervised domain adaptation in person re-identification (re-ID), which aims to transfer knowledge from the source domain to the target domain. Existing methods are primary to reduce the inter-domain shift between the domains, which however usually overlook the relations among target samples. This paper investigates into the intra-domain variations of the target domain and proposes a novel adaptation framework w.r.t three types of underlying invariance, i.e., Exemplar-Invariance, Camera-Invariance, and Neighborhood-Invariance. Specifically, an exemplar memory is introduced to store features of samples, which can effectively and efficiently enforce the invariance constraints over the global dataset. We further present the Graph-based Positive Prediction (GPP) method to explore reliable neighbors for the target domain, which is built upon the memory and is trained on the source samples. Experiments demonstrate that 1) the three invariance properties are complementary and indispensable for effective domain adaptation, 2) the memory plays a key role in implementing invariance learning and improves the performance with limited extra computation cost, 3) GPP can facilitate the invariance learning and thus significantly improves the results, and 4) our approach produces new state-of-the-art adaptation accuracy on three re-ID large-scale benchmarks.

Effect of Social Anxiety on the Adoption of Robotic Training Partner.

With the development of artificial intelligence technologies, robotic training partner is becoming a reality, which is a substitute for human training partner. Socially anxious individuals feel uncomfortable in front of unfamiliar people or when being observed by others. Playing with robotic training partners can avoid face-to-face interaction with other people. It is unclear whether social anxiety affects the adoption of robotic training partners. This study investigates the effect of social anxiety on the adoption of robotic training partners among university students. Study 1 confirmed that university students with higher social anxiety are more likely to choose robotic training partners than human training partners. Mediation analysis in Study 2 supported the mediating role of sense of relaxation with robotic training partner in the positive effect of social anxiety on the adoption of robotic training partner. This study shows that developing training partner robots is a meaningful thing for corporate profits and the health of socially anxious people.

CamStyle: A Novel Data Augmentation Method for Person Re-Identification.

Person re-identification (re-ID) is a cross-camera retrieval task that suffers from image style variations caused by different cameras. The art implicitly addresses this problem by learning a camera-invariant descriptor subspace. In this paper, we explicitly consider this challenge by introducing camera style (CamStyle). CamStyle can serve as a data augmentation approach that reduces the risk of deep network overfitting and that smooths the CamStyle disparities. Specifically, with a style transfer model, labeled training images can be style transferred to each camera, and along with the original training samples, form the augmented training set. This method, while increasing data diversity against overfitting, also incurs a considerable level of noise. In the effort to alleviate the impact of noise, the label smooth regularization (LSR) is adopted. The vanilla version of our method (without LSR) performs reasonably well on few camera systems in which overfitting often occurs. With LSR, we demonstrate consistent improvement in all systems regardless of the extent of overfitting. We also report competitive accuracy compared with the state of the art on Market-1501 and DukeMTMC-re-ID. Importantly, CamStyle can be employed to the challenging problems of one view learning and unsupervised domain adaptation (UDA) in person re-identification (re-ID), both of which have critical research and application significance. The former only has labeled data in one camera view and the latter only has labeled data in the source domain. Experimental results show that CamStyle significantly improves the performance of the baseline in the two problems. Specially, for UDA, CamStyle achieves state-of-the-art accuracy based on a baseline deep re-ID model on Market-1501 and DukeMTMC-reID. Our code is available at: https://github.com/zhunzhong07/CamStyle .

Effects of TESTIN gene expression on proliferation and migration of the 5-8F nasopharyngeal carcinoma cell line.

PURPOSE: To investigate effects of the TESTIN (TES) gene on proliferation and migration of highly metastatic nasopharyngeal carcinoma cell line 5-8F and the related mechanisms. MATERIALS AND METHODS: The target gene of human nasopharyngeal carcinoma cell line 5-8F was amplified by PCR and cloned into the empty plasmid pEGFP-N1 to construct a eukaryotic expression vector pEGFP-N1-TES. This was then transfected into 5-8F cells. MTT assays, flow cytometry and scratch wound tests were used to detect the proliferation and migration of transfected 5-8F cells. RESULTS: A cell model with stable and high expression of TES gene was successfully established. MTT assays showed that the OD value of 5-8F/TES cells was markedly lower than that of 5-8F/GFP cells and 5-8F cells (p<0.05). Flow cytometry showed that the apoptosis rate of 5-8F/TES cells was prominently increased compared with 5-8F/GFP cells and 5-8F cells (p<0.05). In vitro scratch wound assays showed that, the width of the wound area of 5-8F/TES cells narrowed slightly, while the width of the wound area of 5-8F/ GFP cells and 5-8F cells narrowed sharply, suggesting that the TES overexpression could inhibit the migration ability. CONCLUSIONS: TES gene expression remarkably inhibits the proliferation of human nasopharyngeal carcinoma cell line 5-8F and reduces its migration in vitro. Thus, it may be a potential tumor suppressor gene for nasopharyngeal carcinoma.