Kernel Cross-Correlator

Cross-correlator plays a significant role in many visual perception tasks, such as object detection and tracking. Beyond the linear cross-correlator, this paper proposes a kernel cross-correlator (KCC) that breaks traditional limitations. First, by introducing the kernel trick, the KCC extends the linear cross-correlation to non-linear space, which is more robust to signal noises and distortions. Second, the connection to the existing works shows that KCC provides a unified solution for correlation filters. Third, KCC is applicable to any kernel function and is not limited to circulant structure on training data, thus it is able to predict affine transformations with customized properties. Last, by leveraging the fast Fourier transform (FFT), KCC eliminates direct calculation of kernel vectors, thus achieves better performance yet still with a reasonable computational cost. Comprehensive experiments on visual tracking and human activity recognition using wearable devices demonstrate its robustness, flexibility, and efficiency. The source codes of both experiments are released at https://github.com/wang-chen/KCCComment: The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18

CIAN: Cross-Image Affinity Net for Weakly Supervised Semantic Segmentation

Weakly supervised semantic segmentation with only image-level labels saves large human effort to annotate pixel-level labels. Cutting-edge approaches rely on various innovative constraints and heuristic rules to generate the masks for every single image. Although great progress has been achieved by these methods, they treat each image independently and do not take account of the relationships across different images. In this paper, however, we argue that the cross-image relationship is vital for weakly supervised segmentation. Because it connects related regions across images, where supplementary representations can be propagated to obtain more consistent and integral regions. To leverage this information, we propose an end-to-end cross-image affinity module, which exploits pixel-level cross-image relationships with only image-level labels. By means of this, our approach achieves 64.3% and 65.3% mIoU on Pascal VOC 2012 validation and test set respectively, which is a new state-of-the-art result by only using image-level labels for weakly supervised semantic segmentation, demonstrating the superiority of our approach.Comment: 9 pages, 4 figures, AAAI 202

Clinical efficacy of combination of oxaliplatin and vascular intervention in treatment of advanced cervical cancer and related prognostic factors

Purpose: To investigate the therapeutic effect of combination of oxaliplatin and vascular intervention in patients with advanced cervical cancer (ACC), and its influence on the prognosis of patients.Methods: One hundred ACC patients were selected and equally assigned to control (oxaliplatin) and combination or study (oxaliplatin plus vascular intervention) groups. The patients in control group received oxaliplatin, while those in study group were treated with oxaliplatin combined with vascular intervention. Clinical efficacy, levels of vascular endothelial growth factor (VEGF), vascular endothelial growth factor receptor-2 (VEGFR-2), fibroblast growth factor-2 (FGF-2), BFGF and platelet-derived growth factor (PDGF) before and after therapy, and survival rate at 3, 6, 12 and 18 months after therapy were determined compared between the two groups. The prognostic factors were analyzed with logistic factor analysis.Results: The clinical efficacy and survival rate at 3, 6, 12 and 18 months after therapy in the combination group were higher when compared with those of the control group (p < 0.05). After therapy, the levels of VEGF, VEGFR-2, FGF-2, BFGF and PDGF were lower in the combination group than in control group. Age, short-term efficacy and basic diseases were identified as the influencing factors for the prognosis of patients with advanced cervical cancer (p < 0.05).Conclusion: The combination of oxaliplatin and vascular intervention significantly improved clinical treatment efficacy and survival rate in ACC patients. Age, short-term efficacy and basic diseases affected the prognosis of patients

DropPos: Pre-Training Vision Transformers by Reconstructing Dropped Positions

As it is empirically observed that Vision Transformers (ViTs) are quite insensitive to the order of input tokens, the need for an appropriate self-supervised pretext task that enhances the location awareness of ViTs is becoming evident. To address this, we present DropPos, a novel pretext task designed to reconstruct Dropped Positions. The formulation of DropPos is simple: we first drop a large random subset of positional embeddings and then the model classifies the actual position for each non-overlapping patch among all possible positions solely based on their visual appearance. To avoid trivial solutions, we increase the difficulty of this task by keeping only a subset of patches visible. Additionally, considering there may be different patches with similar visual appearances, we propose position smoothing and attentive reconstruction strategies to relax this classification problem, since it is not necessary to reconstruct their exact positions in these cases. Empirical evaluations of DropPos show strong capabilities. DropPos outperforms supervised pre-training and achieves competitive results compared with state-of-the-art self-supervised alternatives on a wide range of downstream benchmarks. This suggests that explicitly encouraging spatial reasoning abilities, as DropPos does, indeed contributes to the improved location awareness of ViTs. The code is publicly available at https://github.com/Haochen-Wang409/DropPos.Comment: Accepted by NeurIPS 202

Using Unreliable Pseudo-Labels for Label-Efficient Semantic Segmentation

The crux of label-efficient semantic segmentation is to produce high-quality pseudo-labels to leverage a large amount of unlabeled or weakly labeled data. A common practice is to select the highly confident predictions as the pseudo-ground-truths for each pixel, but it leads to a problem that most pixels may be left unused due to their unreliability. However, we argue that every pixel matters to the model training, even those unreliable and ambiguous pixels. Intuitively, an unreliable prediction may get confused among the top classes, however, it should be confident about the pixel not belonging to the remaining classes. Hence, such a pixel can be convincingly treated as a negative key to those most unlikely categories. Therefore, we develop an effective pipeline to make sufficient use of unlabeled data. Concretely, we separate reliable and unreliable pixels via the entropy of predictions, push each unreliable pixel to a category-wise queue that consists of negative keys, and manage to train the model with all candidate pixels. Considering the training evolution, we adaptively adjust the threshold for the reliable-unreliable partition. Experimental results on various benchmarks and training settings demonstrate the superiority of our approach over the state-of-the-art alternatives

Klein-bottle quadrupole insulators and Dirac semimetals

The Benalcazar-Bernevig-Hughes (BBH) quadrupole insulator model is a cornerstone model for higher-order topological phases. It requires \pi flux threading through each plaquette of the two-dimensional Su-Schrieffer-Heeger model. Recent studies show that particular \pi-flux patterns can modify the fundamental Brillouin zone from the shape of a torus to a Klein-bottle with emerging topological phases. By designing different \pi-flux patterns, we propose two types of Klein-bottle BBH models. These models show rich topological phases including Klein-bottle quadrupole insulators and Dirac semimetals. The phase with nontrivial Klein-bottle topology shows twined edge modes at open boundaries. These edge modes can further support second-order topology yielding a quadrupole insulator. Remarkably, both models are robust against flux perturbations. Moreover, we show that different \pi-flux patterns dramatically affect the phase diagram of the Klein-bottle BBH models. Going beyond the original BBH model, Dirac semimetal phases emerge in Klein-bottle BBH models featured by the coexistence of twined edge modes and bulk Dirac points.Comment: 11 pages, 11 figure

Control of spectral extreme events in ultrafast fibre lasers by a genetic algorithm

Extreme wave events or rogue waves (RWs) are both statistically rare and of exceptionally large amplitude. They are observed in many complex systems ranging from oceanic and optical environments to financial models and Bose-Einstein condensates. As they appear from nowhere and disappear without a trace, their emergence is unpredictable and non-repetitive, which make them particularly challenging to control. Here, we extend the use of genetic algorithms (GAs), which have been exclusively designed for searching and optimising stationary or repetitive processes in nonlinear optical systems, to the active control of extreme events in a fibre laser cavity. Feeding real-time spectral measurements into a GA controlling the electronics to optimise the cavity parameters, we are able to trigger wave events in the cavity that have the typical statistics of RWs in the frequency domain. This accurate control enables the generation of the optical RWs with a spectral peak intensity 32.8 times higher than the significant intensity threshold. A rationale is proposed and confirmed by numerical simulations of the laser model for the related frequency up- and down-shifting of the optical spectrum that are experimentally observed