Learning Second-Order Attentive Context for Efficient Correspondence Pruning

Correspondence pruning aims to search consistent correspondences (inliers) from a set of putative correspondences. It is challenging because of the disorganized spatial distribution of numerous outliers, especially when putative correspondences are largely dominated by outliers. It's more challenging to ensure effectiveness while maintaining efficiency. In this paper, we propose an effective and efficient method for correspondence pruning. Inspired by the success of attentive context in correspondence problems, we first extend the attentive context to the first-order attentive context and then introduce the idea of attention in attention (ANA) to model second-order attentive context for correspondence pruning. Compared with first-order attention that focuses on feature-consistent context, second-order attention dedicates to attention weights itself and provides an additional source to encode consistent context from the attention map. For efficiency, we derive two approximate formulations for the naive implementation of second-order attention to optimize the cubic complexity to linear complexity, such that second-order attention can be used with negligible computational overheads. We further implement our formulations in a second-order context layer and then incorporate the layer in an ANA block. Extensive experiments demonstrate that our method is effective and efficient in pruning outliers, especially in high-outlier-ratio cases. Compared with the state-of-the-art correspondence pruning approach LMCNet, our method runs 14 times faster while maintaining a competitive accuracy.Comment: 9 pages, 8 figures; Accepted to AAAI 2023 (Oral

Learning Probabilistic Coordinate Fields for Robust Correspondences

We introduce Probabilistic Coordinate Fields (PCFs), a novel geometric-invariant coordinate representation for image correspondence problems. In contrast to standard Cartesian coordinates, PCFs encode coordinates in correspondence-specific barycentric coordinate systems (BCS) with affine invariance. To know \textit{when and where to trust} the encoded coordinates, we implement PCFs in a probabilistic network termed PCF-Net, which parameterizes the distribution of coordinate fields as Gaussian mixture models. By jointly optimizing coordinate fields and their confidence conditioned on dense flows, PCF-Net can work with various feature descriptors when quantifying the reliability of PCFs by confidence maps. An interesting observation of this work is that the learned confidence map converges to geometrically coherent and semantically consistent regions, which facilitates robust coordinate representation. By delivering the confident coordinates to keypoint/feature descriptors, we show that PCF-Net can be used as a plug-in to existing correspondence-dependent approaches. Extensive experiments on both indoor and outdoor datasets suggest that accurate geometric invariant coordinates help to achieve the state of the art in several correspondence problems, such as sparse feature matching, dense image registration, camera pose estimation, and consistency filtering. Further, the interpretable confidence map predicted by PCF-Net can also be leveraged to other novel applications from texture transfer to multi-homography classification.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine Intelligenc

Callous-unemotional traits and child response to teacher rewards, discipline, and instructional methods in Chinese preschools: A classroom observation study

Atypical responses to teacher rewards, discipline and different forms of instructional methods have been identified as potential contributors to disruptive behavior, low school engagement, and academic underachievement in children with elevated callous-unemotional (CU) traits. To date, research on CU traits in schools has relied on interview or questionnaire methods and has predominantly been conducted in Western countries. Thus, the present study aims to investigate the relationships between CU traits and children’s responses to teacher rewards, discipline and instructional methods in the Chinese preschool context using classroom observation. Eight teachers (7 females, 1 male; M = 37.66 years) and 116 children (56% girls; M = 5.16 years) from two mainstream Chinese preschools participated in the study. Of the 116 eligible children, the behavior of 108 children from four classes were observed during classroom activities. Findings indicated that CU traits were not related to children’s responses to discipline, nor did CU traits moderate the relationship between instructional methods and children’s academic engagement. Higher CU traits predicted a greater frequency of one-to-one teacher-child interaction. Our findings offer initial insights into the potential of early school-based interventions in fostering engagement and prosocial behavior among children with CU traits. However, they also highlight the need for additional support for preschool teachers, who face the challenge of managing these high-risk children who appear to require more individual time and attention

Constraining Depth Map Geometry for Multi-View Stereo: A Dual-Depth Approach with Saddle-shaped Depth Cells

Learning-based multi-view stereo (MVS) methods deal with predicting accurate depth maps to achieve an accurate and complete 3D representation. Despite the excellent performance, existing methods ignore the fact that a suitable depth geometry is also critical in MVS. In this paper, we demonstrate that different depth geometries have significant performance gaps, even using the same depth prediction error. Therefore, we introduce an ideal depth geometry composed of Saddle-Shaped Cells, whose predicted depth map oscillates upward and downward around the ground-truth surface, rather than maintaining a continuous and smooth depth plane. To achieve it, we develop a coarse-to-fine framework called Dual-MVSNet (DMVSNet), which can produce an oscillating depth plane. Technically, we predict two depth values for each pixel (Dual-Depth), and propose a novel loss function and a checkerboard-shaped selecting strategy to constrain the predicted depth geometry. Compared to existing methods,DMVSNet achieves a high rank on the DTU benchmark and obtains the top performance on challenging scenes of Tanks and Temples, demonstrating its strong performance and generalization ability. Our method also points to a new research direction for considering depth geometry in MVS.Comment: Accepted by ICCV 202

OR-003 Time-sequential Changes of Myocardial AMPK and PGC-1α Expression during Detraining between High-intensity Interval Training and Moderate-intensity Continuous Training on Wistar Rats

Objective Without appropriate training stimulus, the athlete experiences a loss of the physiological adaptations brought about by exercise. In most of highly trained athletes, short of training induces a rapid decline in VO2max, but it remains above control values. However, there is no specific information for normal people about the effects of detraining during certain period aerobic training. Therefore, the purpose of this study was to examine the effects of detraining between high-intensity interval training (HIIT) and moderate-intensity continuous training (MICT) on myocardial AMPK and PGC-1α expression characterization time-sequential changes in Wistar rats, as well as the changing characteristics between AMPK and PGC-1α expression characterization and cardiacrespiratory fitness (CRF). Moreover, the potential mechanism for exercise arrangement was also investigated. Methods In this study, 27 four-month-old male Wistar rats were randomly divided into the sedentary control group (C), MICT group (M) and HIIT group (H).Animals in the training groups ran on a treadmill 5 days/week during 6 weeks. HIIT group (70%-90%-50%VO2max) and MICT (50%VO2max) group was ran for 50min exercise every training day. All the rats free to gather the food and drinking water. All rats were measured the VO2max after a week adaptive training and then the M group and H group began to exercise intervention. After 6 weeks of training, rats were randomly selected from each group at the 24h, the 3rd day, the 7th day and the 10th day. The Maximal oxygen uptake test was carried out before the samples were taken, and the abdominal aortic blood, myocardium and other tissues were collected after anesthesia. The expression characterization of AMPK and PGC-1α was tested by Western blotting analysis. All statistical analyses were performed using SPSS 17.0 and GraphPad Prism 5.01 for Windows. Data was presented as mean and standard deviation（SD), unless otherwise stated. The two-way ANOVA (intervention × time) with repeated measures were used to analyze differences of HIIT and MICT with time-sequential. One-way ANOVA was used to compare the difference between time-sequential among the groups for each variable. The relationship between variables was assessed using the Pearson correlation coefficient. The expression characterization of the detraining effect was also assessed using Cohen’s d effect sizes (ES) and thresholds (<0.5=small; 0.5~0.79=moderate;≥0.8=large). The level of significance was set at P<0.05 and the confidence intervals at 95%. Results VO2max showed a gradual downward trend in both H and M groups throughout the 10 days detraining periods. Detraining in the 10th day, training cessation resulted in the VO2max of H and M group were significantly lower than detraining 24h. (P<0.05). Detraining in 3rd day, myocardial AMPK and PGC-1α increased in H group, it was significantly higher than the C group (P<0.05), but there is no differences in the other detraining days(P>0.05). Furthermore, detraining in the 7th day myocardial PGC-1α decreased in H group, this value was significantly lower than detraining 24h (P<0.05). Detraining in 7th day, myocardial AMPK and PGC-1α started decreasing, but it was not significant than C group or other detraining days (P>0.05). Conclusions (1)The present data suggest that 6 weeks HIIT and MICT can increase the expression of myocardial AMPK and PGC-1α, the VO2max training effects disappeared after 10 days detraining. (2)Detraining during the 3rd day and the 7th day was the critical time point for retraining, endurance training intervention should be arranging among these days. (3)The VO2max time-sequential changes was partially consistent with AMPK and PGC-1α expression characterization, but AMPK and PGC-1α expression characterization was more sensitive than VO2max

Variation in xylem hydraulic structure and function of two mangrove species across a latitudinal gradient in eastern Australia

Mangroves growing in tropical and subtropical intertidal zones face challenges from warming and altered rainfall patterns associated with global climate change. Intraspecific variation in hydraulic traits may allow a mangrove species to acclimate to novel climatic conditions, yet little is known regarding the potential for adaptive plasticity in these traits. In this study, we aimed to quantify the variation in plant hydraulic traits of two widespread mangrove species growing across a latitudinal gradient. We investigated the xylem hydraulic structure and function of Avicennia marina and Aegiceras corniculatum, across three sites spanning a latitudinal gradient of 17.45◦ in eastern Australia. We found that both species were highly resistant to xylem embolism and that there was significant intraspecific variation in hydraulic traits between sites. The highest embolism resistance and sapwood-specific hydraulic conductivity (KS) were found at the lowest latitude site that had the highest mean annual temperature and precipitation. A. marina showed no differences in vessel size and density among sites. It has other special features such as successive cambia enhancing its ability to adapt to a large environmental gradient. In contrast, A. corniculatum showed higher vessel densities at lower latitudes. There was a significant and positive correlation (R 2 = 0.72, p < 0.05) between KS and embolism resistance across species and sites, suggesting the absence of a tradeoff between hydraulic efficiency and safety. Both embolism resistance and KS were negatively correlated with wood density but positively with vessel wall reinforcement. This study reveals that these two widespread mangrove species were adapted to warmer climates by enhancing both hydraulic efficiency and safety

Silk fibroin microneedle patches for the treatment of insomnia

As a patient-friendly technology, drug-loaded microneedles can deliver drugs through the skin into the body. This system has broad application prospects and is receiving wide attention. Based on the knowledge acquired in this work, we successfully developed a melatonin-loaded microneedle prepared from proline/melatonin/silk fibroin. The engineered microneedles’ morphological, physical, and chemical properties were characterized to investigate their structural transformation mechanism and transdermal drug-delivery capabilities. The results indicated that the crystal structure of silk fibroin in drug-loaded microneedles was mainly Silk I crystal structure, with a low dissolution rate and suitable swelling property. Melatonin-loaded microneedles showed high mechanical properties, and the breaking strength of a single needle was 1.2 N, which could easily be penetrated the skin. The drug release results in vitro revealed that the effective drug concentration was obtained quickly during the early delivery. The successful drug concentration was maintained through continuous release at the later stage. For in vivo experimentation, the Sprague Dawley (SD) rat model of insomnia was constructed. The outcome exhibited that the melatonin-loaded microneedle released the drug into the body through the skin and maintained a high blood concentration (over 5 ng/mL) for 4–6 h. The maximum blood concentration was above 10 ng/mL, and the peak time was 0.31 h. This system indicates that it achieved the purpose of mimicking physiological release and treating insomnia.This work was supported by National Natural Science Foundation of China (Grant No. 51973144), College Nature Science Research Project of Jiangsu Province, China (Grant No. 20KJA540002), PAPD, and Six Talent Peaks Project in Jiangsu Province (Grant No. SWYY-038).SCK is supported by the European Union Framework Programme for Research and Innovation HORIZON 2020 (Grant agreement no. 668983—FoReCaST) and the FCT-Portugal project BREAST-IT (PTDC/BTM-ORG/28168/2017)

Fast Full-frame Video Stabilization with Iterative Optimization

Video stabilization refers to the problem of transforming a shaky video into a visually pleasing one. The question of how to strike a good trade-off between visual quality and computational speed has remained one of the open challenges in video stabilization. Inspired by the analogy between wobbly frames and jigsaw puzzles, we propose an iterative optimization-based learning approach using synthetic datasets for video stabilization, which consists of two interacting submodules: motion trajectory smoothing and full-frame outpainting. First, we develop a two-level (coarse-to-fine) stabilizing algorithm based on the probabilistic flow field. The confidence map associated with the estimated optical flow is exploited to guide the search for shared regions through backpropagation. Second, we take a divide-and-conquer approach and propose a novel multiframe fusion strategy to render full-frame stabilized views. An important new insight brought about by our iterative optimization approach is that the target video can be interpreted as the fixed point of nonlinear mapping for video stabilization. We formulate video stabilization as a problem of minimizing the amount of jerkiness in motion trajectories, which guarantees convergence with the help of fixed-point theory. Extensive experimental results are reported to demonstrate the superiority of the proposed approach in terms of computational speed and visual quality. The code will be available on GitHub.Comment: Accepted by ICCV202