PDANet: Pyramid Density-aware Attention Net for Accurate Crowd Counting

Crowd counting, i.e., estimating the number of people in a crowded area, has attracted much interest in the research community. Although many attempts have been reported, crowd counting remains an open real-world problem due to the vast scale variations in crowd density within the interested area, and severe occlusion among the crowd. In this paper, we propose a novel Pyramid Density-Aware Attention-based network, abbreviated as PDANet, that leverages the attention, pyramid scale feature and two branch decoder modules for density-aware crowd counting. The PDANet utilizes these modules to extract different scale features, focus on the relevant information, and suppress the misleading ones. We also address the variation of crowdedness levels among different images with an exclusive Density-Aware Decoder (DAD). For this purpose, a classifier evaluates the density level of the input features and then passes them to the corresponding high and low crowded DAD modules. Finally, we generate an overall density map by considering the summation of low and high crowded density maps as spatial attention. Meanwhile, we employ two losses to create a precise density map for the input scene. Extensive evaluations conducted on the challenging benchmark datasets well demonstrate the superior performance of the proposed PDANet in terms of the accuracy of counting and generated density maps over the well-known state of the arts

Point Clouds Are Specialized Images: A Knowledge Transfer Approach for 3D Understanding

Self-supervised representation learning (SSRL) has gained increasing attention in point cloud understanding, in addressing the challenges posed by 3D data scarcity and high annotation costs. This paper presents PCExpert, a novel SSRL approach that reinterprets point clouds as "specialized images". This conceptual shift allows PCExpert to leverage knowledge derived from large-scale image modality in a more direct and deeper manner, via extensively sharing the parameters with a pre-trained image encoder in a multi-way Transformer architecture. The parameter sharing strategy, combined with a novel pretext task for pre-training, i.e., transformation estimation, empowers PCExpert to outperform the state of the arts in a variety of tasks, with a remarkable reduction in the number of trainable parameters. Notably, PCExpert's performance under LINEAR fine-tuning (e.g., yielding a 90.02% overall accuracy on ScanObjectNN) has already approached the results obtained with FULL model fine-tuning (92.66%), demonstrating its effective and robust representation capability

NoPe-NeRF: Optimising Neural Radiance Field with No Pose Prior

Training a Neural Radiance Field (NeRF) without pre-computed camera poses is challenging. Recent advances in this direction demonstrate the possibility of jointly optimising a NeRF and camera poses in forward-facing scenes. However, these methods still face difficulties during dramatic camera movement. We tackle this challenging problem by incorporating undistorted monocular depth priors. These priors are generated by correcting scale and shift parameters during training, with which we are then able to constrain the relative poses between consecutive frames. This constraint is achieved using our proposed novel loss functions. Experiments on real-world indoor and outdoor scenes show that our method can handle challenging camera trajectories and outperforms existing methods in terms of novel view rendering quality and pose estimation accuracy. Our project page is https://nope-nerf.active.vision

Detection-driven exposure-correction network for nighttime drone-view object detection.

Drone-view object detection (DroneDet) models typically suffer a significant performance drop when applied to nighttime scenes. Existing solutions attempt to employ an exposure-adjustment module to reveal objects hidden in dark regions before detection. However, most exposure-adjustment models are only optimized for human perception, where the exposure-adjusted images may not necessarily enhance recognition. To tackle this issue, we propose a novel Detection-driven Exposure-Correction network for nighttime DroneDet, called DEDet. The DEDet conducts adaptive, non-linear adjustment of pixel values in a spatially fine-grained manner to generate DroneDet-friendly images. Specifically, we develop a Fine-grained Parameter Predictor (FPP) to estimate pixel-wise parameter maps of the image filters. These filters, along with the estimated parameters, are used to adjust pixel values of the low-light image based on non-uniform illuminations in drone-captured images. In order to learn the non-linear transformation from the original nighttime images to their DroneDet-friendly counterparts, we propose a Progressive Filtering module that applies recursive filters to iteratively refine the exposed image. Furthermore, to evaluate the performance of the proposed DEDet, we have built a dataset NightDrone to address the scarcity of the datasets specifically tailored for this purpose. Extensive experiments conducted on four nighttime datasets show that DEDet achieves a superior accuracy compared with the state-of-the-art methods. Furthermore, ablation studies and visualizations demonstrate the validity and interpretability of our approach. Our NightDrone dataset can be downloaded from https://github.com/yuexiemail/NightDrone-Dataset

Clinical and molecular features of thiazide-induced hyponatremia

Hypertension affects more than 30% of the world’s adult population and thiazide (and thiazide-like) diuretics are amongst the most widely used, effective and least costly treatments available, with all-cause mortality benefits equivalent to ACE inhibitors or calcium channel antagonists. A minority of patients develop Thiazide Induced Hyponatremia (TIH) and this is largely unpredictable at the point of thiazide prescription. In some cases TIH can cause debilitating symptoms and require hospital admission. Although TIH affects only a minority of patients exposed to thiazides, the high prevalence of hypertension leads to TIH being the most common cause of drug-induced hyponatremia requiring hospital admission in the UK. This review examines current clinical and scientific understanding of TIH. Consideration is given to demographic associations, limitations of current electrolyte monitoring regimens, clinical presentation, the phenotype evident on routine clinical blood and urine tests as well as more extensive analyses of blood and urine in research settings, recent genetic associations with TIH and thoughts on management of the condition. Recent genetic and phenotyping analysis has suggested that prostaglandin E2 pathways in the collecting duct may have a role in the development of TIH in a subgroup of patients. Greater understanding of the molecular pathophysiology of TIH raises the prospect of pre-prescription TIH risk profiling and may offer novel insights into how TIH may be avoided, prevented and treated. The rising prevalence of hypertension and the widespread use of thiazides mean that further understanding of TIH will continue to be a pressing issue for patients, physicians and scientists alike for the foreseeable future

DRL-GAN: dual-stream representation learning GAN for low-resolution image classification in UAV applications.

Identifying tiny objects from extremely low resolution (LR) UAV-based remote sensing images is generally considered as a very challenging task, because of very limited information in the object areas. In recent years, there have been very limited attempts to approach this problem. These attempts intend to deal with LR image classification by enhancing either the poor image quality or image representations. In this paper, we argue that the performance improvement in LR image classification is affected by the inconsistency of the information loss and learning priority on Low-Frequency (LF) components and High-Frequency (HF) components. To address this LF-HF inconsistency problem, we propose a Dual-Stream Representation Learning Generative Adversarial Network (DRL-GAN).The core idea is to produce super image representations optimal for LR recognition by simultaneously recovering the missing information in LF and HF components, respectively, under the guidance of high-resolution (HR) images. We evaluate the performance of DRL-GAN on the challenging task of LR image classification. A comparison of the experimental results on the LR benchmark, namely HRSC and CIFAR-10, and our newly collected “WIDER-SHIP” dataset demonstrates the effectiveness of our DRL-GAN, which significantly improves the classification performance, with up to 10% gain on average