scholarly journals Adversarial Feature Disentanglement for Long-Term Person Re-identification

2021 ◽  
Author(s):  
Wanlu Xu ◽  
Hong Liu ◽  
Wei Shi ◽  
Ziling Miao ◽  
Zhisheng Lu ◽  
...  
Keyword(s):  
Real World ◽  
Large Scale ◽  
State Of The Art ◽  
Feeding Strategy ◽  
Feature Space ◽  
Image Space ◽  
Original Image ◽  
Short Term ◽  
Identification Methods

Most existing person re-identification methods are effective in short-term scenarios because of their appearance dependencies. However, these methods may fail in long-term scenarios where people might change their clothes. To this end, we propose an adversarial feature disentanglement network (AFD-Net) which contains intra-class reconstruction and inter-class adversary to disentangle the identity-related and identity-unrelated (clothing) features. For intra-class reconstruction, the person images with the same identity are represented and disentangled into identity and clothing features by two separate encoders, and further reconstructed into original images to reduce intra-class feature variations. For inter-class adversary, the disentangled features across different identities are exchanged and recombined to generate adversarial clothes-changing images for training, which makes the identity and clothing features more independent. Especially, to supervise these new generated clothes-changing images, a re-feeding strategy is designed to re-disentangle and reconstruct these new images for image-level self-supervision in the original image space and feature-level soft-supervision in the disentangled feature space. Moreover, we collect a challenging Market-Clothes dataset and a real-world PKU-Market-Reid dataset for evaluation. The results on one large-scale short-term dataset (Market-1501) and five long-term datasets (three public and two we proposed) confirm the superiority of our method against other state-of-the-art methods.

scholarly journals Documentary data and the study of past droughts: a global state of the art

2018 ◽  
Vol 14 (12) ◽  
pp. 1915-1960 ◽  
Author(s):  
Rudolf Brázdil ◽  
Andrea Kiss ◽  
Jürg Luterbacher ◽  
David J. Nash ◽  
Ladislava Řezníčková
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Drought Indices ◽  
Documentary Evidence ◽  
Climatic Trends ◽  
Instrumental Observations ◽  
Spatio Temporal ◽  
Epigraphic Evidence ◽  
Administrative Evidence

Abstract. The use of documentary evidence to investigate past climatic trends and events has become a recognised approach in recent decades. This contribution presents the state of the art in its application to droughts. The range of documentary evidence is very wide, including general annals, chronicles, memoirs and diaries kept by missionaries, travellers and those specifically interested in the weather; records kept by administrators tasked with keeping accounts and other financial and economic records; legal-administrative evidence; religious sources; letters; songs; newspapers and journals; pictographic evidence; chronograms; epigraphic evidence; early instrumental observations; society commentaries; and compilations and books. These are available from many parts of the world. This variety of documentary information is evaluated with respect to the reconstruction of hydroclimatic conditions (precipitation, drought frequency and drought indices). Documentary-based drought reconstructions are then addressed in terms of long-term spatio-temporal fluctuations, major drought events, relationships with external forcing and large-scale climate drivers, socio-economic impacts and human responses. Documentary-based drought series are also considered from the viewpoint of spatio-temporal variability for certain continents, and their employment together with hydroclimate reconstructions from other proxies (in particular tree rings) is discussed. Finally, conclusions are drawn, and challenges for the future use of documentary evidence in the study of droughts are presented.

scholarly journals Extrinsic Camera Calibration with Line-Laser Projection

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1091
Author(s):  
Izaak Van Crombrugge ◽  
Rudi Penne ◽  
Steve Vanlanduit
Keyword(s):  
Camera Calibration ◽  
Real World ◽  
Large Scale ◽  
State Of The Art ◽  
Bundle Adjustment ◽  
Field Of View ◽  
Extrinsic Calibration ◽  
Practical Procedure ◽  
Partial Overlap

Knowledge of precise camera poses is vital for multi-camera setups. Camera intrinsics can be obtained for each camera separately in lab conditions. For fixed multi-camera setups, the extrinsic calibration can only be done in situ. Usually, some markers are used, like checkerboards, requiring some level of overlap between cameras. In this work, we propose a method for cases with little or no overlap. Laser lines are projected on a plane (e.g., floor or wall) using a laser line projector. The pose of the plane and cameras is then optimized using bundle adjustment to match the lines seen by the cameras. To find the extrinsic calibration, only a partial overlap between the laser lines and the field of view of the cameras is needed. Real-world experiments were conducted both with and without overlapping fields of view, resulting in rotation errors below 0.5°. We show that the accuracy is comparable to other state-of-the-art methods while offering a more practical procedure. The method can also be used in large-scale applications and can be fully automated.

scholarly journals Two Stage Continuous Gesture Recognition Based on Deep Learning

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 534
Author(s):  
Huogen Wang
Keyword(s):  
Gesture Recognition ◽  
Large Scale ◽  
Short Term Memory ◽  
Short Term ◽  
Hand Motion ◽  
Spatiotemporal Features ◽  
Spatiotemporal Information ◽  
Video Frames ◽  
Depth Sequences

The paper proposes an effective continuous gesture recognition method, which includes two modules: segmentation and recognition. In the segmentation module, the video frames are divided into gesture frames and transitional frames by using the information of hand motion and appearance, and continuous gesture sequences are segmented into isolated sequences. In the recognition module, our method exploits the spatiotemporal information embedded in RGB and depth sequences. For the RGB modality, our method adopts Convolutional Long Short-Term Memory Networks to learn long-term spatiotemporal features from short-term spatiotemporal features obtained from a 3D convolutional neural network. For the depth modality, our method converts a sequence into Dynamic Images and Motion Dynamic Images through weighted rank pooling and feed them into Convolutional Neural Networks, respectively. Our method has been evaluated on both ChaLearn LAP Large-scale Continuous Gesture Dataset and Montalbano Gesture Dataset and achieved state-of-the-art performance.

Bistability of somatic pattern memories: stochastic outcomes in bioelectric circuits underlying regeneration

2021 ◽  
Vol 376 (1821) ◽  
pp. 20190765 ◽  
Author(s):  
Giovanni Pezzulo ◽  
Joshua LaPalme ◽  
Fallon Durant ◽  
Michael Levin
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
State Of The Art ◽  
Memory Representation ◽  
Theme Issue ◽  
Evolutionary Innovation ◽  
New Interpretation ◽  
The Brain

Nervous systems’ computational abilities are an evolutionary innovation, specializing and speed-optimizing ancient biophysical dynamics. Bioelectric signalling originated in cells' communication with the outside world and with each other, enabling cooperation towards adaptive construction and repair of multicellular bodies. Here, we review the emerging field of developmental bioelectricity, which links the field of basal cognition to state-of-the-art questions in regenerative medicine, synthetic bioengineering and even artificial intelligence. One of the predictions of this view is that regeneration and regulative development can restore correct large-scale anatomies from diverse starting states because, like the brain, they exploit bioelectric encoding of distributed goal states—in this case, pattern memories. We propose a new interpretation of recent stochastic regenerative phenotypes in planaria, by appealing to computational models of memory representation and processing in the brain. Moreover, we discuss novel findings showing that bioelectric changes induced in planaria can be stored in tissue for over a week, thus revealing that somatic bioelectric circuits in vivo can implement a long-term, re-writable memory medium. A consideration of the mechanisms, evolution and functionality of basal cognition makes novel predictions and provides an integrative perspective on the evolution, physiology and biomedicine of information processing in vivo . This article is part of the theme issue ‘Basal cognition: multicellularity, neurons and the cognitive lens’.

scholarly journals Learning Long- and Short-Term User Literal-Preference with Multimodal Hierarchical Transformer Network for Personalized Image Caption

2020 ◽  
Vol 34 (05) ◽  
pp. 9571-9578 ◽  
Author(s):  
Wei Zhang ◽  
Yue Ying ◽  
Pan Lu ◽  
Hongyuan Zha
Keyword(s):  
State Of The Art ◽  
Natural Extension ◽  
Target Image ◽  
Short Term ◽  
Image Representations ◽  
High Level ◽  
Image Descriptions ◽  
Shed Light ◽  
Image Caption

Personalized image caption, a natural extension of the standard image caption task, requires to generate brief image descriptions tailored for users' writing style and traits, and is more practical to meet users' real demands. Only a few recent studies shed light on this crucial task and learn static user representations to capture their long-term literal-preference. However, it is insufficient to achieve satisfactory performance due to the intrinsic existence of not only long-term user literal-preference, but also short-term literal-preference which is associated with users' recent states. To bridge this gap, we develop a novel multimodal hierarchical transformer network (MHTN) for personalized image caption in this paper. It learns short-term user literal-preference based on users' recent captions through a short-term user encoder at the low level. And at the high level, the multimodal encoder integrates target image representations with short-term literal-preference, as well as long-term literal-preference learned from user IDs. These two encoders enjoy the advantages of the powerful transformer networks. Extensive experiments on two real datasets show the effectiveness of considering two types of user literal-preference simultaneously and better performance over the state-of-the-art models.

scholarly journals Learning from Interventions Using Hierarchical Policies for Safe Learning

2020 ◽  
Vol 34 (06) ◽  
pp. 10352-10360
Author(s):  
Jing Bi ◽  
Vikas Dhiman ◽  
Tianyou Xiao ◽  
Chenliang Xu
Keyword(s):  
Reaction Time ◽  
State Of The Art ◽  
The State ◽  
Policy Framework ◽  
Asymptotic Performance ◽  
Short Term ◽  
Learning From Demonstrations ◽  
Hierarchical Levels ◽  
Long Term Behavior

Learning from Demonstrations (LfD) via Behavior Cloning (BC) works well on multiple complex tasks. However, a limitation of the typical LfD approach is that it requires expert demonstrations for all scenarios, including those in which the algorithm is already well-trained. The recently proposed Learning from Interventions (LfI) overcomes this limitation by using an expert overseer. The expert overseer only intervenes when it suspects that an unsafe action is about to be taken. Although LfI significantly improves over LfD, the state-of-the-art LfI fails to account for delay caused by the expert's reaction time and only learns short-term behavior. We address these limitations by 1) interpolating the expert's interventions back in time, and 2) by splitting the policy into two hierarchical levels, one that generates sub-goals for the future and another that generates actions to reach those desired sub-goals. This sub-goal prediction forces the algorithm to learn long-term behavior while also being robust to the expert's reaction time. Our experiments show that LfI using sub-goals in a hierarchical policy framework trains faster and achieves better asymptotic performance than typical LfD.

scholarly journals Long-term target tracking combined with re-detection

2020 ◽  
Author(s):  
Juanjuan Wang ◽  
HaoRan Yang ◽  
Ning Xu ◽  
Chengqin Wu ◽  
ZengShun Zhao ◽  
...  
Keyword(s):  
Correlation Energy ◽  
State Of The Art ◽  
Tracking Algorithm ◽  
Correlation Filters ◽  
Short Term ◽  
Tracking Method ◽  
Tracking Tasks ◽  
Svm Model ◽  
Confidence Degree

Abstract The long-term visual tracking undergoes more challenges and is closer to realistic applications than short-term tracking. However, the performances of most existing methods have been limited in the long-term tracking tasks. In this work, we present a reliable yet simple long-term tracking method, which extends the state-of-the-art Learning Adaptive Discriminative Correlation Filters (LADCF) tracking algorithm with a re-detection component based on the SVM model. The LADCF tracking algorithm localizes the target in each frame and the re-detector is able to efficiently re-detect the target in the whole image when the tracking fails. We further introduce a robust confidence degree evaluation criterion that combines the maximum response criterion and the average peak-to correlation energy (APCE) to judge the confidence level of the predicted target. When the confidence degree is generally high, the SVM is updated accordingly. If the confidence drops sharply, the SVM re-detects the target. We perform extensive experiments on the OTB-2015 and UAV123 datasets. The experimental results demonstrate the effectiveness of our algorithm in long-term tracking.

scholarly journals Challenges in modelling isoprene and monoterpene emission dynamics of Arctic plants: a case study from a subarctic tundra heath

2016 ◽  
Vol 13 (24) ◽  
pp. 6651-6667 ◽  
Author(s):  
Jing Tang ◽  
Guy Schurgers ◽  
Hanna Valolahti ◽  
Patrick Faubert ◽  
Päivi Tiiva ◽  
...  
Keyword(s):  
Response Curve ◽  
Large Scale ◽  
Ecosystem Model ◽  
The Arctic ◽  
Short Term ◽  
Response Curves ◽  
Arctic Plants ◽  
Scale Modelling

Abstract. The Arctic is warming at twice the global average speed, and the warming-induced increases in biogenic volatile organic compounds (BVOCs) emissions from Arctic plants are expected to be drastic. The current global models' estimations of minimal BVOC emissions from the Arctic are based on very few observations and have been challenged increasingly by field data. This study applied a dynamic ecosystem model, LPJ-GUESS, as a platform to investigate short-term and long-term BVOC emission responses to Arctic climate warming. Field observations in a subarctic tundra heath with long-term (13-year) warming treatments were extensively used for parameterizing and evaluating BVOC-related processes (photosynthesis, emission responses to temperature and vegetation composition). We propose an adjusted temperature (T) response curve for Arctic plants with much stronger T sensitivity than the commonly used algorithms for large-scale modelling. The simulated emission responses to 2 °C warming between the adjusted and original T response curves were evaluated against the observed warming responses (WRs) at short-term scales. Moreover, the model responses to warming by 4 and 8 °C were also investigated as a sensitivity test. The model showed reasonable agreement to the observed vegetation CO2 fluxes in the main growing season as well as day-to-day variability of isoprene and monoterpene emissions. The observed relatively high WRs were better captured by the adjusted T response curve than by the common one. During 1999–2012, the modelled annual mean isoprene and monoterpene emissions were 20 and 8 mg C m−2 yr−1, with an increase by 55 and 57 % for 2 °C summertime warming, respectively. Warming by 4 and 8 °C for the same period further elevated isoprene emission for all years, but the impacts on monoterpene emissions levelled off during the last few years. At hour-day scale, the WRs seem to be strongly impacted by canopy air T, while at the day–year scale, the WRs are a combined effect of plant functional type (PFT) dynamics and instantaneous BVOC responses to warming. The identified challenges in estimating Arctic BVOC emissions are (1) correct leaf T estimation, (2) PFT parameterization accounting for plant emission features as well as physiological responses to warming, and (3) representation of long-term vegetation changes in the past and the future.

scholarly journals Legal Judgment Prediction Based on Multiclass Information Fusion

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Kongfan Zhu ◽  
Rundong Guo ◽  
Weifeng Hu ◽  
Zeqiang Li ◽  
Yujun Li
Keyword(s):  
Information Fusion ◽  
Real World ◽  
Large Scale ◽  
State Of The Art ◽  
External Information ◽  
Criminal Cases ◽  
Law System ◽  
Large Scale Dataset ◽  
Assistant Systems ◽  
Civil Law System

Legal judgment prediction (LJP), as an effective and critical application in legal assistant systems, aims to determine the judgment results according to the information based on the fact determination. In real-world scenarios, to deal with the criminal cases, judges not only take advantage of the fact description, but also consider the external information, such as the basic information of defendant and the court view. However, most existing works take the fact description as the sole input for LJP and ignore the external information. We propose a Transformer-Hierarchical-Attention-Multi-Extra (THME) Network to make full use of the information based on the fact determination. We conduct experiments on a real-world large-scale dataset of criminal cases in the civil law system. Experimental results show that our method outperforms state-of-the-art LJP methods on all judgment prediction tasks.

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