scholarly journals Viewpoint-Aware Loss with Angular Regularization for Person Re-Identification

2020 ◽  
Vol 34 (07) ◽  
pp. 13114-13121 ◽  
Author(s):  
Zhihui Zhu ◽  
Xinyang Jiang ◽  
Feng Zheng ◽  
Xiaowei Guo ◽  
Feiyue Huang ◽  
...  
Keyword(s):  
State Of The Art ◽  
The State ◽  
Feature Representation ◽  
Novel Approach ◽  
Cluster Label ◽  
Feature Spaces ◽  
Great Progress

Although great progress in supervised person re-identification (Re-ID) has been made recently, due to the viewpoint variation of a person, Re-ID remains a massive visual challenge. Most existing viewpoint-based person Re-ID methods project images from each viewpoint into separated and unrelated sub-feature spaces. They only model the identity-level distribution inside an individual viewpoint but ignore the underlying relationship between different viewpoints. To address this problem, we propose a novel approach, called Viewpoint-Aware Loss with Angular Regularization (VA-reID). Instead of one subspace for each viewpoint, our method projects the feature from different viewpoints into a unified hypersphere and effectively models the feature distribution on both the identity-level and the viewpoint-level. In addition, rather than modeling different viewpoints as hard labels used for conventional viewpoint classification, we introduce viewpoint-aware adaptive label smoothing regularization (VALSR) that assigns the adaptive soft label to feature representation. VALSR can effectively solve the ambiguity of the viewpoint cluster label assignment. Extensive experiments on the Market1501 and DukeMTMC-reID datasets demonstrated that our method outperforms the state-of-the-art supervised Re-ID methods.

scholarly journals Density Guarantee on Finding Multiple Subgraphs and Subtensors

2021 ◽  
Vol 15 (5) ◽  
pp. 1-32
Author(s):  
Quang-huy Duong ◽  
Heri Ramampiaro ◽  
Kjetil Nørvåg ◽  
Thu-lan Dam
Keyword(s):  
Lower Bound ◽  
State Of The Art ◽  
The State ◽  
The Other ◽  
Exact Methods ◽  
Practical Solution ◽  
Novel Approach ◽  
Wide Range ◽  
Real World Datasets ◽  
Tensor Data

Dense subregion (subgraph & subtensor) detection is a well-studied area, with a wide range of applications, and numerous efficient approaches and algorithms have been proposed. Approximation approaches are commonly used for detecting dense subregions due to the complexity of the exact methods. Existing algorithms are generally efficient for dense subtensor and subgraph detection, and can perform well in many applications. However, most of the existing works utilize the state-or-the-art greedy 2-approximation algorithm to capably provide solutions with a loose theoretical density guarantee. The main drawback of most of these algorithms is that they can estimate only one subtensor, or subgraph, at a time, with a low guarantee on its density. While some methods can, on the other hand, estimate multiple subtensors, they can give a guarantee on the density with respect to the input tensor for the first estimated subsensor only. We address these drawbacks by providing both theoretical and practical solution for estimating multiple dense subtensors in tensor data and giving a higher lower bound of the density. In particular, we guarantee and prove a higher bound of the lower-bound density of the estimated subgraph and subtensors. We also propose a novel approach to show that there are multiple dense subtensors with a guarantee on its density that is greater than the lower bound used in the state-of-the-art algorithms. We evaluate our approach with extensive experiments on several real-world datasets, which demonstrates its efficiency and feasibility.

scholarly journals Redundancy Removal Adversarial Active Learning Based on Norm Online Uncertainty Indicator

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jifeng Guo ◽  
Zhiqi Pang ◽  
Wenbo Sun ◽  
Shi Li ◽  
Yu Chen
Keyword(s):  
Active Learning ◽  
Feature Vector ◽  
State Of The Art ◽  
Representation Learning ◽  
The State ◽  
Feature Representation ◽  
Learning Ability ◽  
Redundancy Removal ◽  
Previous State ◽  
Uncertainty Score

Active learning aims to select the most valuable unlabelled samples for annotation. In this paper, we propose a redundancy removal adversarial active learning (RRAAL) method based on norm online uncertainty indicator, which selects samples based on their distribution, uncertainty, and redundancy. RRAAL includes a representation generator, state discriminator, and redundancy removal module (RRM). The purpose of the representation generator is to learn the feature representation of a sample, and the state discriminator predicts the state of the feature vector after concatenation. We added a sample discriminator to the representation generator to improve the representation learning ability of the generator and designed a norm online uncertainty indicator (Norm-OUI) to provide a more accurate uncertainty score for the state discriminator. In addition, we designed an RRM based on a greedy algorithm to reduce the number of redundant samples in the labelled pool. The experimental results on four datasets show that the state discriminator, Norm-OUI, and RRM can improve the performance of RRAAL, and RRAAL outperforms the previous state-of-the-art active learning methods.

scholarly journals Learning to Transfer Relational Representations through Analogy

2019 ◽  
Vol 33 ◽  
pp. 10015-10016
Author(s):  
Gaetano Rossiello ◽  
Alfio Gliozzo ◽  
Michael Glass
Keyword(s):  
State Of The Art ◽  
Relation Extraction ◽  
Knowledge Bases ◽  
The State ◽  
Large Set ◽  
Relational Information ◽  
Siamese Network ◽  
Distant Supervision ◽  
Novel Approach ◽  
Art Methods

We propose a novel approach to learn representations of relations expressed by their textual mentions. In our assumption, if two pairs of entities belong to the same relation, then those two pairs are analogous. We collect a large set of analogous pairs by matching triples in knowledge bases with web-scale corpora through distant supervision. This dataset is adopted to train a hierarchical siamese network in order to learn entity-entity embeddings which encode relational information through the different linguistic paraphrasing expressing the same relation. The model can be used to generate pre-trained embeddings which provide a valuable signal when integrated into an existing neural-based model by outperforming the state-of-the-art methods on a relation extraction task.

scholarly journals Semantics-Aligned Representation Learning for Person Re-Identification

2020 ◽  
Vol 34 (07) ◽  
pp. 11173-11180 ◽  
Author(s):  
Xin Jin ◽  
Cuiling Lan ◽  
Wenjun Zeng ◽  
Guoqiang Wei ◽  
Zhibo Chen
Keyword(s):  
State Of The Art ◽  
Representation Learning ◽  
The State ◽  
Feature Representation ◽  
Texture Image ◽  
Computationally Efficient ◽  
Feature Maps ◽  
Benchmark Datasets ◽  
Texture Generation ◽  
Base Network

Person re-identification (reID) aims to match person images to retrieve the ones with the same identity. This is a challenging task, as the images to be matched are generally semantically misaligned due to the diversity of human poses and capture viewpoints, incompleteness of the visible bodies (due to occlusion), etc. In this paper, we propose a framework that drives the reID network to learn semantics-aligned feature representation through delicate supervision designs. Specifically, we build a Semantics Aligning Network (SAN) which consists of a base network as encoder (SA-Enc) for re-ID, and a decoder (SA-Dec) for reconstructing/regressing the densely semantics aligned full texture image. We jointly train the SAN under the supervisions of person re-identification and aligned texture generation. Moreover, at the decoder, besides the reconstruction loss, we add Triplet ReID constraints over the feature maps as the perceptual losses. The decoder is discarded in the inference and thus our scheme is computationally efficient. Ablation studies demonstrate the effectiveness of our design. We achieve the state-of-the-art performances on the benchmark datasets CUHK03, Market1501, MSMT17, and the partial person reID dataset Partial REID.

scholarly journals Classifier for Activities with Variations

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3529 ◽  
Author(s):  
Rabih Younes ◽  
Mark Jones ◽  
Thomas Martin
Keyword(s):  
Activity Recognition ◽  
State Of The Art ◽  
The State ◽  
Daily Activities ◽  
Controlled Environment ◽  
Novel Approach ◽  
Specific Activities ◽  
Application Specific ◽  
Complex Activities ◽  
Better Than

Most activity classifiers focus on recognizing application-specific activities that are mostly performed in a scripted manner, where there is very little room for variation within the activity. These classifiers are mainly good at recognizing short scripted activities that are performed in a specific way. In reality, especially when considering daily activities, humans perform complex activities in a variety of ways. In this work, we aim to make activity recognition more practical by proposing a novel approach to recognize complex heterogeneous activities that could be performed in a wide variety of ways. We collect data from 15 subjects performing eight complex activities and test our approach while analyzing it from different aspects. The results show the validity of our approach. They also show how it performs better than the state-of-the-art approaches that tried to recognize the same activities in a more controlled environment.

scholarly journals Unsupervised Chunking Based on Graph Propagation from Bilingual Corpus

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Ling Zhu ◽  
Derek F. Wong ◽  
Lidia S. Chao
Keyword(s):  
Knowledge Transfer ◽  
Chinese Text ◽  
State Of The Art ◽  
The State ◽  
Label Propagation ◽  
Novel Approach ◽  
Shallow Parsing ◽  
Graph Propagation ◽  
Experimental Comparisons

This paper presents a novel approach for unsupervised shallow parsing model trained on the unannotated Chinese text of parallel Chinese-English corpus. In this approach, no information of the Chinese side is applied. The exploitation of graph-based label propagation for bilingual knowledge transfer, along with an application of using the projected labels as features in unsupervised model, contributes to a better performance. The experimental comparisons with the state-of-the-art algorithms show that the proposed approach is able to achieve impressive higher accuracy in terms ofF-score.

scholarly journals A Review on Bitumen Rejuvenation: Mechanisms, Materials, Methods and Perspectives

2019 ◽  
Vol 9 (20) ◽  
pp. 4316 ◽  
Author(s):  
Loise ◽  
Caputo ◽  
Porto ◽  
Calandra ◽  
Angelico ◽  
...  
Keyword(s):  
Complex Systems ◽  
Chemical Structure ◽  
Structural Changes ◽  
Ad Hoc ◽  
State Of The Art ◽  
Chemical Interaction ◽  
The State ◽  
X Ray ◽  
X Ray Scattering ◽  
Novel Approach

This review aims to explore the state of the knowledge and the state-of-the-art regarding bitumen rejuvenation. In particular, attention was paid to clear things up about the rejuvenator mechanism of action. Frequently, the terms rejuvenator and flux oil, or oil (i.e., softening agent) are used as if they were synonymous. According to our knowledge, these two terms refer to substances producing different modifications to the aged bitumen: they can decrease the viscosity (softening agents), or, in addition to this, restore the original microstructure (real rejuvenators). In order to deal with the argument in its entirety, the bitumen is investigated in terms of chemical structure and microstructural features. Proper investigating tools are, therefore, needed to distinguish the different mechanisms of action of the various types of bitumen, so attention is focused on recent research and the use of different investigation techniques to distinguish between various additives. Methods based on organic synthesis can also be used to prepare ad-hoc rejuvenating molecules with higher performances. The interplay of chemical interaction, structural changes and overall effect of the additive is then presented in terms of the modern concepts of complex systems, which furnishes valid arguments to suggest X-ray scattering and Nuclear Magnetic Resonance relaxometry experiments as vanguard and forefront tools to study bitumen. Far from being a standard review, this work represents a critical analysis of the state-of-the-art taking into account for the molecular basis at the origin of the observed behavior. Furnishing a novel viewpoint for the study of bitumen based on the concepts of the complex systems in physics, it constitutes a novel approach for the study of these systems.

scholarly journals Learning to Embed Sentences Using Attentive Recursive Trees

2019 ◽  
Vol 33 ◽  
pp. 6991-6998
Author(s):  
Jiaxin Shi ◽  
Lei Hou ◽  
Juanzi Li ◽  
Zhiyuan Liu ◽  
Hanwang Zhang
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
The State ◽  
Feature Representation ◽  
Tree Structure ◽  
Tree Model ◽  
Training Strategy ◽  
Recursive Tree ◽  
Recursive Trees ◽  
Embedding Methods

Sentence embedding is an effective feature representation for most deep learning-based NLP tasks. One prevailing line of methods is using recursive latent tree-structured networks to embed sentences with task-specific structures. However, existing models have no explicit mechanism to emphasize taskinformative words in the tree structure. To this end, we propose an Attentive Recursive Tree model (AR-Tree), where the words are dynamically located according to their importance in the task. Specifically, we construct the latent tree for a sentence in a proposed important-first strategy, and place more attentive words nearer to the root; thus, AR-Tree can inherently emphasize important words during the bottomup composition of the sentence embedding. We propose an end-to-end reinforced training strategy for AR-Tree, which is demonstrated to consistently outperform, or be at least comparable to, the state-of-the-art sentence embedding methods on three sentence understanding tasks.

scholarly journals Estimating 6D Aircraft Pose from Keypoints and Structures

2021 ◽  
Vol 13 (4) ◽  
pp. 663
Author(s):  
Runze Fan ◽  
Ting-Bing Xu ◽  
Zhenzhong Wei
Keyword(s):  
Open Access ◽  
Pose Estimation ◽  
State Of The Art ◽  
The State ◽  
Estimation Methods ◽  
Geometric Information ◽  
Comparative Performance ◽  
Topological Relationship ◽  
Novel Approach ◽  
Rgb Image

This article addresses the challenge of 6D aircraft pose estimation from a single RGB image during the flight. Many recent works have shown that keypoints-based approaches, which first detect keypoints and then estimate the 6D pose, achieve remarkable performance. However, it is hard to locate the keypoints precisely in complex weather scenes. In this article, we propose a novel approach, called Pose Estimation with Keypoints and Structures (PEKS), which leverages multiple intermediate representations to estimate the 6D pose. Unlike previous works, our approach simultaneously locates keypoints and structures to recover the pose parameter of aircraft through a Perspective-n-Point Structure (PnPS) algorithm. These representations integrate the local geometric information of the object and the topological relationship between components of the target, which effectively improve the accuracy and robustness of 6D pose estimation. In addition, we contribute a dataset for aircraft pose estimation which consists of 3681 real images and 216,000 rendered images. Extensive experiments on our own aircraft pose dataset and multiple open-access pose datasets (e.g., ObjectNet3D, LineMOD) demonstrate that our proposed method can accurately estimate 6D aircraft pose in various complex weather scenes while achieving the comparative performance with the state-of-the-art pose estimation methods.

scholarly journals POS-Taggging Malay Corpus: A Novel Approach Based on Maximum Entropy

2018 ◽  
Vol 7 (3.20) ◽  
pp. 6
Author(s):  
Juhaida Abu Bakar ◽  
Khairuddin Khairuddin ◽  
Mohammad Faidzul Nasrudin ◽  
Mohd Zamri Murah
Keyword(s):  
Maximum Entropy ◽  
Text Analysis ◽  
State Of The Art ◽  
The State ◽  
The Past ◽  
Novel Approach ◽  
Proposed Model ◽  
Automated Text Analysis ◽  
Development Of Language ◽  
Language Technologies

Jawi and Roman scripts are represented Malay language. In the past, Jawi writings are widely used by the Malay community and foreigners; and it can be seen in the old documents. Old documents face the risk of background damage. In order to preserve this valuable information, there are significant needs to automated Jawi materials. Based on previous literature, POS-tags are known as the first phase in the automated text analysis; and the development of language technologies can barely initiate without this phase. We highlight the existing POS-tags approaches; and suggest the development of Malay Jawi POS-tags using extended ME-based approach on NUWT Corpus. Results have shown that the proposed model yielded a higher accuracy in comparison to the state-of-the-art model.  

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