Robust Visual Tracking Revisited: From Correlation Filter to Template Matching

In this paper, we propose a novel matching based tracker by investigating the relationship between template matching and the recent popular correlation filter based trackers (CFTs). Compared to the correlation operation in CFTs, a sophisticated similarity metric termed "mutual buddies similarity" (MBS) is proposed to exploit the relationship of multiple reciprocal nearest neighbors for target matching. By doing so, our tracker obtains powerful discriminative ability on distinguishing target and background as demonstrated by both empirical and theoretical analyses. Besides, instead of utilizing single template with the improper updating scheme in CFTs, we design a novel online template updating strategy named "memory filtering" (MF), which aims to select a certain amount of representative and reliable tracking results in history to construct the current stable and expressive template set. This scheme is beneficial for the proposed tracker to comprehensively "understand" the target appearance variations, "recall" some stable results. Both qualitative and quantitative evaluations on two benchmarks suggest that the proposed tracking method performs favorably against some recently developed CFTs and other competitive trackers.Comment: has been published on IEEE TI

Combined effect of dydrogesterone and letrozole on humoral immune function, sex hormone levels and serology-related indices in patients with endometriosis

Purpose: To investigate the combined effect of dydrogesterone and letrozole on humoral immunity, and levels of sex hormones and serology-related indices in patients with endometriosis.Methods: Patients with endometriosis (98 cases) were randomly assigned to two groups of 49 patients each: control group and study group. The control group received dydrogesterone (10 mg/kg) orally from the fifth day to the twenty-fifth day of the patient's menstrual cycle, twice daily; while the study group, in addition to dydrogesterone (10 mg/kg), received letrozole (2.5 mg/kg). Treatment in both groups lasted one month, and changes in the levels of humoral immunity, sex hormones and serology-related indices were evaluated before and after treatment. Clinical effectiveness and adverse reactions in both groups were also assessed.Results: After treatment, total effectiveness was markedly higher in the study group (91.84 %) than in control group (77.55 %, p < 0.05). Post-treatment, humoral immunity (IgM, IgG and C3) levels were significantly higher in the study group than in control group (p < 0.05) while estradiol (E2) level in both groups were significantly reduced; however, E2 level was markedly lower in the study group than in control group (p < 0.05).Conclusion: These results suggest that the combination of dydrogesterone with letrozole alleviates clinical symptoms of endometriosis, improves humoral immune function, and maintains sex hormone levels.Keywords: Endometriosis, Dydrogesterone, Letrozole, Humoral immunity, Sex hormones, Serologicalrelated indice

SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data

Data mixing augmentation has proved effective in training deep models. Recent methods mix labels mainly based on the mixture proportion of image pixels. As the main discriminative information of a fine-grained image usually resides in subtle regions, methods along this line are prone to heavy label noise in fine-grained recognition. We propose in this paper a novel scheme, termed as Semantically Proportional Mixing (SnapMix), which exploits class activation map (CAM) to lessen the label noise in augmenting fine-grained data. SnapMix generates the target label for a mixed image by estimating its intrinsic semantic composition, and allows for asymmetric mixing operations and ensures semantic correspondence between synthetic images and target labels. Experiments show that our method consistently outperforms existing mixed-based approaches on various datasets and under different network depths. Furthermore, by incorporating the mid-level features, the proposed SnapMix achieves top-level performance, demonstrating its potential to serve as a solid baseline for fine-grained recognition. Our code is available at https://github.com/Shaoli-Huang/SnapMix.git.Comment: Accepted by AAAI202

Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images

In hyperspectral remote sensing data mining, it is important to take into account of both spectral and spatial information, such as the spectral signature, texture feature and morphological property, to improve the performances, e.g., the image classification accuracy. In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier. However, multiple features from various domains definitely have different physical meanings and statistical properties, and thus such concatenation hasn't efficiently explore the complementary properties among different features, which should benefit for boost the feature discriminability. Furthermore, it is also difficult to interpret the transformed results of the concatenated vector. Consequently, finding a physically meaningful consensus low dimensional feature representation of original multiple features is still a challenging task. In order to address the these issues, we propose a novel feature learning framework, i.e., the simultaneous spectral-spatial feature selection and extraction algorithm, for hyperspectral images spectral-spatial feature representation and classification. Specifically, the proposed method learns a latent low dimensional subspace by projecting the spectral-spatial feature into a common feature space, where the complementary information has been effectively exploited, and simultaneously, only the most significant original features have been transformed. Encouraging experimental results on three public available hyperspectral remote sensing datasets confirm that our proposed method is effective and efficient