“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

This paper contributes a new high quality dataset for person re-identification, named "Market-1501". Generally, current datasets: 1) are limited in scale, 2) consist of hand-drawn bboxes, which are unavailable under realistic settings, 3) have only one ground truth and one query image for each identity (close environment). To tackle these problems, the proposed Market-1501 dataset is featured in three aspects. First, it contains over 32,000 annotated bboxes, plus a distractor set of over 500K images, making it the largest person re-id dataset to date. Second, images in Market-1501 dataset are produced using the Deformable Part Model (DPM) as pedestrian detector. Third, our dataset is collected in an open system, where each identity has multiple images under each camera. As a minor contribution, inspired by recent advances in large-scale image search, this paper proposes an unsupervised Bag-of-Words descriptor. We view person re-identification as a special task of image search. In experiment, we show that the proposed descriptor yields competitive accuracy on VIPeR, CUHK03, and Market-1501 datasets, and is scalable on the large-scale 500k dataset.

/pdf/scalable-person-re-identification-a-benchmark-iiee63lja2.pdf

Scalable Person Re-identification: A Benchmark

The author's preface gives an outline: "This book is about weakconvergence methods in metric spaces, with applications sufficient to show their power and utility. The Introduction motivates the definitions and indicates how the theory will yield solutions to problems arising outside it. Chapter 1 sets out the basic general theorems, which are then specialized in Chapter 2 to the space C[0, l ] of continuous functions on the unit interval and in Chapter 3 to the space D [0, 1 ] of functions with discontinuities of the first kind. The results of the first three chapters are used in Chapter 4 to derive a variety of limit theorems for dependent sequences of random variables. " The book develops and expands on Donsker's 1951 and 1952 papers on the invariance principle and empirical distributions. The basic random variables remain real-valued although, of course, measures on C[0, l ] and D[0, l ] are vitally used. Within this framework, there are various possibilities for a different and apparently better treatment of the material. More of the general theory of weak convergence of probabilities on separable metric spaces would be useful. Metrizability of the convergence is not brought up until late in the Appendix. The close relation of the Prokhorov metric and a metric for convergence in probability is (hence) not mentioned (see V. Strassen, Ann. Math. Statist. 36 (1965), 423-439; the reviewer, ibid. 39 (1968), 1563-1572). This relation would illuminate and organize such results as Theorems 4.1, 4.2 and 4.4 which give isolated, ad hoc connections between weak convergence of measures and nearness in probability. In the middle of p. 16, it should be noted that C*(S) consists of signed measures which need only be finitely additive if 5 is not compact. On p. 239, where the author twice speaks of separable subsets having nonmeasurable cardinal, he means "discrete" rather than "separable." Theorem 1.4 is Ulam's theorem that a Borel probability on a complete separable metric space is tight. Theorem 1 of Appendix 3 weakens completeness to topological completeness. After mentioning that probabilities on the rationals are tight, the author says it is an

Convergence of probability measures

Recent advances in convolutional neural networks

The PASCAL Visual Object Classes Challenge

Recent researches on unsupervised person re-identification~(reID) have demonstrated that pre-training on unlabeled person images achieves superior performance on downstream reID tasks than pre-training on ImageNet. However, those pre-trained methods are specifically designed for reID and suffer flexible adaption to other pedestrian analysis tasks. In this paper, we propose VAL-PAT, a novel framework that learns transferable representations to enhance various pedestrian analysis tasks with multimodal information. To train our framework, we introduce three learning objectives, \emph{i.e.,} self-supervised contrastive learning, image-text contrastive learning and multi-attribute classification. The self-supervised contrastive learning facilitates the learning of the intrinsic pedestrian properties, while the image-text contrastive learning guides the model to focus on the appearance information of pedestrians.Meanwhile, multi-attribute classification encourages the model to recognize attributes to excavate fine-grained pedestrian information. We first perform pre-training on LUPerson-TA dataset, where each image contains text and attribute annotations, and then transfer the learned representations to various downstream tasks, including person reID, person attribute recognition and text-based person search. Extensive experiments demonstrate that our framework facilitates the learning of general pedestrian representations and thus leads to promising results on various pedestrian analysis tasks.

Learning Transferable Pedestrian Representation from Multimodal Information Supervision

The main goal of active voltage control is to keep the voltage of each bus in the grid within a safe range. With the increasing penetration of renewable and distributed energy sources in the grid, grow-ing complexity, increasing uncertainty, and aggravating volatility bring great challenges to voltage control in modern power systems. Traditional algorithms can hardly guarantee real-time safe control to cope with these challenges. In recent years, substantial attention has been paid to the application of multi-agent reinforcement learning algorithms (MARL) to coordinate the control units in each area of the grid in real time for active voltage control in complex scenarios. However, these MARL algorithms do not explicitly guarantee that the power system satisfies the security constraints. There is a little in-depth study on safe multi-agent policy learning in multi-agent-based voltage control, especially the direct correction of unsafe actions. In this paper, we formalize the active voltage control problem as a Constrained Markov Game and approach it with a centralized data-driven safety layer that requires global observations and maps unsafe actions to safe actions. In order to make the policy network rely on local observations for decentralized execution, we introduce two novel components into the policy network: action correction penalty loss and action correction sub-networks. Notably, our approaches are easily extendable to other MARL algorithms for continuous actions. In the experiments, we evaluate our methods in the power distribution network simulation environment and demonstrate the capability of the safety layer to correct unsafe actions and the effectiveness of the safety layer to improve the performance of the policy itself.

Multi-Agent Reinforcement Learning with Safety Layer for Active Voltage Control

Rate-distortion optimization has been a successful and significant method in video coding. By introducing Lagrange multiplier optimization into compress procedure, we can choose coding parameters simply and effectively. In nearest neighbor search problem, hashing has been a popular method to reduce computation and storage cost, which is consistent with video coding method. Conventionally, we evaluate a hashing method with mAP (mean average precision) w.r.t. different bit number, but leave bit cost as an independent measure index. In this paper, we make an attempt to combine retrieval accuracy and bit cost to make evaluation more comprehensive, using the concept of rate distortion optimization. Consequently, we obtain an evaluation criterion to judge which work point of a specific hashing method is better, taking both the accuracy and the bit cost into account. The exertion of an algorithm can be then determined.

Quasi rate distortion optimization for binary hashing

Asymmetric stereoscopic video coding, where one view has a higher quality than the other, has been researched widely. In order to balance the quality difference between views, it has been suggested to alternate the higher-quality view periodically. However, it has also been found out that inter-view prediction is more effective when the source of prediction is the higher-quality view. In this paper, it is proposed to change the inter-view prediction direction periodically together with gradual view refresh. The proposal was implemented on top of the reference software for the multiview and multiview-plus-depth extensions of the High Efficiency Video Coding (H.265/HEVC) standard. Experimental results show that up to 9.1% and an average of 3.1% bitrate reduction are achieved with respect to the MPEG Anchor.

http://ieeexplore.ieee.org/iel7/6662533/6676627/06676636.pdf

View order alternation symmetrization and temporal inter-view combined prediction for 3D video coding

/pdf/ccnet-cluster-coordinated-net-for-learning-multi-agent-1mapjciz7y.pdf

Houqiang Li

Papers

Learning Transferable Pedestrian Representation from Multimodal Information Supervision

Multi-Agent Reinforcement Learning with Safety Layer for Active Voltage Control

Quasi rate distortion optimization for binary hashing

View order alternation symmetrization and temporal inter-view combined prediction for 3D video coding

CCNet: Cluster-Coordinated Net for Learning Multi-agent Communication Protocols with Reinforcement Learning.