scholarly journals SAN-GAL: Spatial Attention Network Guided by Attribute Label for Person Re-identification

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Shaoqi Hou ◽  
Chunhui Liu ◽  
Kangning Yin ◽  
Yiyin Ding ◽  
Zhiguo Wang ◽  
...  
Keyword(s):  
Spatial Attention ◽  
Binary Classification ◽  
Test Results ◽  
Global Features ◽  
Matching Problem ◽  
Heat Map ◽  
Attention Network ◽  
Fine Grained ◽  
Attribute Classification ◽  
High Degree

Person Re-identification (Re-ID) is aimed at solving the matching problem of the same pedestrian at a different time and in different places. Due to the cross-device condition, the appearance of different pedestrians may have a high degree of similarity; at this time, using the global features of pedestrians to match often cannot achieve good results. In order to solve these problems, we designed a Spatial Attention Network Guided by Attribute Label (SAN-GAL), which is a dual-trace network containing both attribute classification and Re-ID. Different from the previous approach of simply adding a branch of attribute binary classification network, our SAN-GAL is mainly divided into two connecting steps. First, with attribute labels as guidance, we generate Attribute Attention Heat map (AAH) through Grad-CAM algorithm to accurately locate fine-grained attribute areas of pedestrians. Then, the Attribute Spatial Attention Module (ASAM) is constructed according to the AHH which is taken as the prior knowledge and introduced into the Re-ID network to assist in the discrimination of the Re-ID task. In particular, our SAN-GAL network can integrate the local attribute information and global ID information of pedestrians without introducing additional attribute region annotation, which has good flexibility and adaptability. The test results on Market1501 and DukeMTMC-reID show that our SAN-GAL can achieve good results and can achieve 85.8% Rank-1 accuracy on DukeMTMC-reID dataset, which is obviously competitive compared with most Re-ID algorithms.

scholarly journals Pose-Guided Multi-Granularity Attention Network for Text-Based Person Search

2020 ◽  
Vol 34 (07) ◽  
pp. 11189-11196 ◽  
Author(s):  
Ya Jing ◽  
Chenyang Si ◽  
Junbo Wang ◽  
Wei Wang ◽  
Liang Wang ◽  
...  
Keyword(s):  
State Of The Art ◽  
Image Database ◽  
Body Part ◽  
Matching Problem ◽  
Attention Network ◽  
Fine Grained ◽  
Person Description ◽  
Person Search ◽  
Semantic Alignment ◽  
Semantic Relevance

Text-based person search aims to retrieve the corresponding person images in an image database by virtue of a describing sentence about the person, which poses great potential for various applications such as video surveillance. Extracting visual contents corresponding to the human description is the key to this cross-modal matching problem. Moreover, correlated images and descriptions involve different granularities of semantic relevance, which is usually ignored in previous methods. To exploit the multilevel corresponding visual contents, we propose a pose-guided multi-granularity attention network (PMA). Firstly, we propose a coarse alignment network (CA) to select the related image regions to the global description by a similarity-based attention. To further capture the phrase-related visual body part, a fine-grained alignment network (FA) is proposed, which employs pose information to learn latent semantic alignment between visual body part and textual noun phrase. To verify the effectiveness of our model, we perform extensive experiments on the CUHK Person Description Dataset (CUHK-PEDES) which is currently the only available dataset for text-based person search. Experimental results show that our approach outperforms the state-of-the-art methods by 15 % in terms of the top-1 metric.

scholarly journals Dual Branch Attention Network for Person Re-Identification

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5839
Author(s):  
Denghua Fan ◽  
Liejun Wang ◽  
Shuli Cheng ◽  
Yongming Li
Keyword(s):  
Local Features ◽  
Attention Mechanism ◽  
Camera Tracking ◽  
Global Features ◽  
Attention Network ◽  
Fine Grained ◽  
Rank List ◽  
Security Problem ◽  
Spatial Dimensions ◽  
Global And Local

As a sub-direction of image retrieval, person re-identification (Re-ID) is usually used to solve the security problem of cross camera tracking and monitoring. A growing number of shopping centers have recently attempted to apply Re-ID technology. One of the development trends of related algorithms is using an attention mechanism to capture global and local features. We notice that these algorithms have apparent limitations. They only focus on the most salient features without considering certain detailed features. People’s clothes, bags and even shoes are of great help to distinguish pedestrians. We notice that global features usually cover these important local features. Therefore, we propose a dual branch network based on a multi-scale attention mechanism. This network can capture apparent global features and inconspicuous local features of pedestrian images. Specifically, we design a dual branch attention network (DBA-Net) for better performance. These two branches can optimize the extracted features of different depths at the same time. We also design an effective block (called channel, position and spatial-wise attention (CPSA)), which can capture key fine-grained information, such as bags and shoes. Furthermore, based on ID loss, we use complementary triplet loss and adaptive weighted rank list loss (WRLL) on each branch during the training process. DBA-Net can not only learn semantic context information of the channel, position, and spatial dimensions but can integrate detailed semantic information by learning the dependency relationships between features. Extensive experiments on three widely used open-source datasets proved that DBA-Net clearly yielded overall state-of-the-art performance. Particularly on the CUHK03 dataset, the mean average precision (mAP) of DBA-Net achieved 83.2%.

Spatial Variability of Contaminant Transport in a Fractured Till, Avedøre Denmark

1999 ◽  
Vol 30 (4-5) ◽  
pp. 333-360 ◽  
Author(s):  
Larry McKay ◽  
Johnny Fredericia ◽  
Melissa Lenczewski ◽  
Jørn Morthorst ◽  
Knud Erik S. Klint
Keyword(s):  
Hydraulic Conductivity ◽  
Spatial Variability ◽  
Field Experiment ◽  
Contaminant Transport ◽  
Tracer Test ◽  
Conductivity Measurements ◽  
Fine Grained ◽  
Downward Migration ◽  
Rapid Transport ◽  
High Degree

A field experiment shows that rapid downward migration of solutes and microorganisms can occur in a fractured till. A solute tracer, chloride, and a bacteriophage tracer, PRD-1, were added to groundwater and allowed to infiltrate downwards over a 4 × 4 m area. Chloride was detected in horizontal filters at 2.0 m depth within 3-40 days of the start of the tracer test, and PRD-1 was detected in the same filters within 0.27 - 27 days. At 2.8 m depth chloride appeared in all the filters, but PRD-1 appeared in only about one-third of the filters. At 4.0 m depth chloride appeared in about one-third of the filters and trace amounts of PRD-1 were detected in only 2 of the 36 filters. Transport rates and peak tracer concentrations decreased with depth, but at each depth there was a high degree of variability. The transport data is generally consistent with expectations based on hydraulic conductivity measurements and on the observed density of fractures and biopores, both of which decrease with depth. Transport of chloride was apparently retarded by diffusion into the fine-grained matrix between fractures, but the rapid transport of PRD-1, with little dispersion, indicates that it was transported mainly through the fractures.

scholarly journals CorrNet: Fine-Grained Emotion Recognition for Video Watching Using Wearable Physiological Sensors

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 52
Author(s):  
Tianyi Zhang ◽  
Abdallah El Ali ◽  
Chen Wang ◽  
Alan Hanjalic ◽  
Pablo Cesar
Keyword(s):  
Emotion Recognition ◽  
Electrodermal Activity ◽  
Short Form ◽  
Recognition Performance ◽  
Binary Classification ◽  
Wearable Sensors ◽  
Recognition Algorithm ◽  
Fine Grained ◽  
Physiological Sensors ◽  
Valence And Arousal

Recognizing user emotions while they watch short-form videos anytime and anywhere is essential for facilitating video content customization and personalization. However, most works either classify a single emotion per video stimuli, or are restricted to static, desktop environments. To address this, we propose a correlation-based emotion recognition algorithm (CorrNet) to recognize the valence and arousal (V-A) of each instance (fine-grained segment of signals) using only wearable, physiological signals (e.g., electrodermal activity, heart rate). CorrNet takes advantage of features both inside each instance (intra-modality features) and between different instances for the same video stimuli (correlation-based features). We first test our approach on an indoor-desktop affect dataset (CASE), and thereafter on an outdoor-mobile affect dataset (MERCA) which we collected using a smart wristband and wearable eyetracker. Results show that for subject-independent binary classification (high-low), CorrNet yields promising recognition accuracies: 76.37% and 74.03% for V-A on CASE, and 70.29% and 68.15% for V-A on MERCA. Our findings show: (1) instance segment lengths between 1–4 s result in highest recognition accuracies (2) accuracies between laboratory-grade and wearable sensors are comparable, even under low sampling rates (≤64 Hz) (3) large amounts of neutral V-A labels, an artifact of continuous affect annotation, result in varied recognition performance.

Modelling of Flow Properties of Asphalt Mastics

2013 ◽  
Vol 752 ◽  
pp. 209-216 ◽  
Author(s):  
Róbert Géber ◽  
László A. Gömze
Keyword(s):  
Research Work ◽  
Road Construction ◽  
Test Results ◽  
Flow Properties ◽  
Rheological Models ◽  
Fine Grained ◽  
Mineral Particles ◽  
Asphalt Mastics ◽  
Rheological Modelling ◽  
Mineral Fillers

The present research work deals with the examination and rheological modelling of flow properties of asphalt mastics which are the most important components of asphalt concretes. Asphalt mastics are mixtures of fine grained mineral filler particles (d<0,063 mm) and bitumen, having a stabilizing role in asphalt mixtures and largely determining the cohesion between mineral particles and bitumen. During our examinations two types of mineral fillers – limestone and dolomite – as well as standard bitumen were tested, which are extensively used in Hungarian road construction. Asphalt mastic mixtures were prepared out of these materials and they were tested with dynamic shear rheometer (DSR). According to the test results, rheological models of mastics were determined. It has been established that at different test temperatures and shear rate ranges asphalt mastics behave as Herschel-Bulkley and Bingham-type materials.

scholarly journals Compressibility behaviour of remoulded, fine-grained soils and correlation with index properties

2000 ◽  
Vol 37 (3) ◽  
pp. 712-722 ◽  
Author(s):  
A Sridharan ◽  
H B Nagaraj
Keyword(s):  
Liquid Limit ◽  
Plasticity Index ◽  
Engineering Properties ◽  
Test Results ◽  
Index Properties ◽  
Initial Water ◽  
Fine Grained ◽  
Wide Range ◽  
The Relationship ◽  
Water Contents

Correlating engineering properties with index properties has assumed greater significance in the recent past in the field of geotechnical engineering. Although attempts have been made in the past to correlate compressibility with various index properties individually, all the properties affecting compressibility behaviour have not been considered together in any single study to examine which index property of the soil correlates best with compressibility behaviour, especially within a set of test results. In the present study, 10 soils covering a sufficiently wide range of liquid limit, plastic limit, and shrinkage limit were selected and conventional consolidation tests were carried out starting with their initial water contents almost equal to their respective liquid limits. The compressibility behaviour is vastly different for pairs of soils having nearly the same liquid limit, but different plasticity characteristics. The relationship between void ratio and consolidation pressure is more closely related to the shrinkage index (shrinkage index = liquid limit - shrinkage limit) than to the plasticity index. Wide variations are seen with the liquid limit. For the soils investigated, the compression index relates better with the shrinkage index than with the plasticity index or liquid limit.Key words: Atterberg limits, classification, clays, compressibility, laboratory tests.

Sign in / Sign up

Export Citation Format

Share Document