Visible-infrared person re-identification (VI-ReID) is a cross-modality retrieval problem, which aims at matching the same pedestrian between the visible and infrared cameras. Due to the existence of pose variation, occlusion, and huge visual differences between the two modalities, previous studies mainly focus on learning image-level shared features. Since they usually learn a global representation or extract uniformly divided part features, these methods are sensitive to misalignments. In this paper, we propose a structure-aware positional transformer (SPOT) network to learn semantic-aware sharable modality features by utilizing the structural and positional information. It consists of two main components: attended structure representation (ASR) and transformer-based part interaction (TPI). Specifically, ASR models the modality-invariant structure feature for each modality and dynamically selects the discriminative appearance regions under the guidance of the structure information. TPI mines the part-level appearance and position relations with a transformer to learn discriminative part-level modality features. With a weighted combination of ASR and TPI, the proposed SPOT explores the rich contextual and structural information, effectively reducing cross-modality difference and enhancing the robustness against misalignments. Extensive experiments indicate that SPOT is superior to the state-of-the-art methods on two cross-modal datasets. Notably, the Rank-1/mAP value on the SYSU-MM01 dataset has improved by 8.43%/6.80%. 

Structure-Aware Positional Transformer for Visible-Infrared Person Re-Identification

Bringing AI To Edge: From Deep Learning’s Perspective

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Bringing AI to edge: From deep learning’s perspective

Artificial Intelligence (AI) at the edge is the utilization of AI in real-world devices. Edge AI refers to the practice of doing AI computations near the users at the network's edge, instead of centralised location like a cloud service provider's data centre. With the latest innovations in AI efficiency, the proliferation of Internet of Things (IoT) devices, and the rise of edge computing, the potential of edge AI has now been unlocked. This study provides a thorough analysis of AI approaches and capabilities as they pertain to edge computing, or Edge AI. Further, a detailed survey of edge computing and its paradigms including transition to Edge AI is presented to explore the background of each variant proposed for implementing Edge Computing. Furthermore, we discussed the Edge AI approach to deploying AI algorithms and models on edge devices, which are typically resource-constrained devices located at the edge of the network. We also presented the technology used in various modern IoT applications, including autonomous vehicles, smart homes, industrial automation, healthcare, and surveillance. Moreover, the discussion of leveraging machine learning algorithms optimized for resource-constrained environments is presented. Finally, important open challenges and potential research directions in the field of edge computing and edge AI have been identified and investigated. We hope that this article will serve as a common goal for a future blueprint that will unite important stakeholders and facilitates to accelerate development in the field of Edge AI. 

Edge AI: A survey

To minimize the average latency of users to complete tasks, this letter jointly optimizes offloading decision, computation and bandwidth resource allocation for Mobile Edge Computing (MEC) networks via partial offloading. Since the considered problem is strongly non-convex with coupled variables, we decompose the original problem into two subproblems: (1) offloading decision; (2) communication bandwidth and computation resources allocation, and further employ Block Coordinate Descent (BCD) method to tackle them sequentially with linear computation complexity. Simulation results demonstrate that the proposed algorithm can achieve better performance on the average latency, and converge speedily.

Latency-Aware Offloading for Mobile Edge Computing Networks

Edge computing and artificial intelligence (AI), especially deep learning for nowadays, are gradually intersecting to build a novel system, called edge intelligence. However, the development of edge intelligence systems encounters some challenges, and one of these challenges is the \textit{computational gap} between computation-intensive deep learning algorithms and less-capable edge systems. Due to the computational gap, many edge intelligence systems cannot meet the expected performance requirements. To bridge the gap, a plethora of deep learning techniques and optimization methods are proposed in the past years: light-weight deep learning models, network compression, and efficient neural architecture search. Although some reviews or surveys have partially covered this large body of literature, we lack a systematic and comprehensive review to discuss all aspects of these deep learning techniques which are critical for edge intelligence implementation. As various and diverse methods which are applicable to edge systems are proposed intensively, a holistic review would enable edge computing engineers and community to know the state-of-the-art deep learning techniques which are instrumental for edge intelligence and to facilitate the development of edge intelligence systems. This paper surveys the representative and latest deep learning techniques that are useful for edge intelligence systems, including hand-crafted models, model compression, hardware-aware neural architecture search and adaptive deep learning models. Finally, based on observations and simple experiments we conducted, we discuss some future directions.

Bringing AI To Edge: From Deep Learning's Perspective

Edge systems integrated with deep neural networks (DNNs) are deemed to pave the way for future artificial intelligence (AI). However, designing accurate and efficient DNNs for resource-limited edge systems is challenging as well as requires a huge amount of engineering efforts from human experts since the design space is highly complex and diverse. Also, previous works mostly focus on designing DNNs with less floating-point operations (FLOPs), but indirect FLOPs count does not necessarily reflect the complexity of DNNs. To tackle these, we, in this paper, propose a novel neural architecture search (NAS) approach, namely EdgeNAS, to automatically discover efficient DNNs for less capable edge systems. To this end, we propose an end-to-end learning-based latency estimator, which is able to directly approximate the architecture latency on edge systems while incurring negligible computational overheads. Further, we effectively incorporate the latency estimator into EdgeNAS with a uniform sampling strategy, which guides the architecture search towards an edge-efficient direction. Moreover, a search space regularization approach is introduced to balance the trade-off between efficiency and accuracy. We evaluate EdgeNAS on the edge platform, Nvidia Jetson Xavier, with three popular datasets. Experimental results demonstrate the superiority of EdgeNAS over state-of-the-art approaches in terms of latency, accuracy, number of parameters, and the search cost.

EdgeNAS: Discovering Efficient Neural Architectures for Edge Systems

Person re-identification (ReID) remains an open-ended research topic, with its variety of substantial applications such as tracking, searching, etc. Existing methods mostly explore the highest-semantic feature embedding, ignoring the insights hidden among the earlier layers. Moreover, owing to the misalignment and pose variations, pose-related information is of great significance and needs to be comprehensively utilized. In this paper, we present a novel person ReID framework called Pose-aware Multi-semantic Fusion Network (PMFN). First, taking into account multiple semantics, we propose Multi-semantic Fusion Network (MFN) as the backbone, employing several shortcuts to reserve bypass feature maps for subsequent fusion. Second, to learn a pose-sensitive embedding, pose-aware clues are considered, forming the complete PMFN and investigating the well-aligned global and local body regions. Finally, the center loss is introduced for enhancing the feature discriminability. Exhaustive experiments on two large-scale person ReID benchmarks demonstrate the strengths of our approach over recent state-of-the-art works.

Person Re-Identification Via Pose-Aware Multi-Semantic Learning

Neural architecture search (NAS) is an emerging paradigm to automate the design of competitive deep neural networks (DNNs). In practice, DNNs are subject to strict latency constraints and any violation may lead to catastrophic consequences (e.g., autonomous vehicles). However, to obtain the architecture that strictly satisfies the required latency constraint, previous hardware-aware differentiable NAS methods have to repeat a plethora of search runs to tune relevant hyperparameters by trial and error, and as a result, the total design cost increases proportionally (empirically by ten times). To tackle this, we, in this article, introduce a lightweight and scalable hardware-aware NAS framework named LightNAS, which consists of two separate stages. In the first stage, we strive to search for the architecture that strictly satisfies the required latency constraint at the macro level in a differentiable manner, and more importantly, through a one-time search (i.e., you only search once). The architectures searched in the first stage are denoted as LightNets. After that, in the second stage, we introduce an efficient evolutionary scheme to further explore the micro-level channel configuration of each LightNet at low cost. To achieve this, we propose an effective yet computationally cheap proxy, namely, batchwise training estimation (BTE), as a plug-in complement to enable the channel-level exploration of LightNets on the fly such that the accuracy of LightNets can be improved without degrading the runtime latency on target hardware. Finally, extensive experiments are conducted on one popular embedded platform (i.e., Nvidia Jetson AGX Xavier) to demonstrate the efficacy of the proposed approach over previous state-of-the-art counterparts.

Xiangzhong Luo

Papers

Bringing AI To Edge: From Deep Learning's Perspective

Bringing AI To Edge: From Deep Learning’s Perspective

EdgeNAS: Discovering Efficient Neural Architectures for Edge Systems

Person Re-Identification Via Pose-Aware Multi-Semantic Learning

LightNAS: On Lightweight and Scalable Neural Architecture Search for Embedded Platforms