Gradual Network for Single Image De-raining

Most advances in single image de-raining meet a key challenge, which is removing rain streaks with different scales and shapes while preserving image details. Existing single image de-raining approaches treat rain-streak removal as a process of pixel-wise regression directly. However, they are lacking in mining the balance between over-de-raining (e.g. removing texture details in rain-free regions) and under-de-raining (e.g. leaving rain streaks). In this paper, we firstly propose a coarse-to-fine network called Gradual Network (GraNet) consisting of coarse stage and fine stage for delving into single image de-raining with different granularities. Specifically, to reveal coarse-grained rain-streak characteristics (e.g. long and thick rain streaks/raindrops), we propose a coarse stage by utilizing local-global spatial dependencies via a local-global subnetwork composed of region-aware blocks. Taking the residual result (the coarse de-rained result) between the rainy image sample (i.e. the input data) and the output of coarse stage (i.e. the learnt rain mask) as input, the fine stage continues to de-rain by removing the fine-grained rain streaks (e.g. light rain streaks and water mist) to get a rain-free and well-reconstructed output image via a unified contextual merging sub-network with dense blocks and a merging block. Solid and comprehensive experiments on synthetic and real data demonstrate that our GraNet can significantly outperform the state-of-the-art methods by removing rain streaks with various densities, scales and shapes while keeping the image details of rain-free regions well-preserved.Comment: In Proceedings of the 27th ACM International Conference on Multimedia (MM 2019

Diverse Cotraining Makes Strong Semi-Supervised Segmentor

Deep co-training has been introduced to semi-supervised segmentation and achieves impressive results, yet few studies have explored the working mechanism behind it. In this work, we revisit the core assumption that supports co-training: multiple compatible and conditionally independent views. By theoretically deriving the generalization upper bound, we prove the prediction similarity between two models negatively impacts the model's generalization ability. However, most current co-training models are tightly coupled together and violate this assumption. Such coupling leads to the homogenization of networks and confirmation bias which consequently limits the performance. To this end, we explore different dimensions of co-training and systematically increase the diversity from the aspects of input domains, different augmentations and model architectures to counteract homogenization. Our Diverse Co-training outperforms the state-of-the-art (SOTA) methods by a large margin across different evaluation protocols on the Pascal and Cityscapes. For example. we achieve the best mIoU of 76.2%, 77.7% and 80.2% on Pascal with only 92, 183 and 366 labeled images, surpassing the previous best results by more than 5%.Comment: ICCV2023, Camera Ready Version, Code: \url{https://github.com/williamium3000/diverse-cotraining

SmooSeg: Smoothness Prior for Unsupervised Semantic Segmentation

Unsupervised semantic segmentation is a challenging task that segments images into semantic groups without manual annotation. Prior works have primarily focused on leveraging prior knowledge of semantic consistency or priori concepts from self-supervised learning methods, which often overlook the coherence property of image segments. In this paper, we demonstrate that the smoothness prior, asserting that close features in a metric space share the same semantics, can significantly simplify segmentation by casting unsupervised semantic segmentation as an energy minimization problem. Under this paradigm, we propose a novel approach called SmooSeg that harnesses self-supervised learning methods to model the closeness relationships among observations as smoothness signals. To effectively discover coherent semantic segments, we introduce a novel smoothness loss that promotes piecewise smoothness within segments while preserving discontinuities across different segments. Additionally, to further enhance segmentation quality, we design an asymmetric teacher-student style predictor that generates smoothly updated pseudo labels, facilitating an optimal fit between observations and labeling outputs. Thanks to the rich supervision cues of the smoothness prior, our SmooSeg significantly outperforms STEGO in terms of pixel accuracy on three datasets: COCOStuff (+14.9%), Cityscapes (+13.0%), and Potsdam-3 (+5.7%).Comment: Accepted by NeurIPS 2023. Code available: https://github.com/mc-lan/SmooSe

Consistent Targets Provide Better Supervision in Semi-supervised Object Detection

In this study, we dive deep into the inconsistency of pseudo targets in semi-supervised object detection (SSOD). Our core observation is that the oscillating pseudo targets undermine the training of an accurate semi-supervised detector. It not only inject noise into student training but also lead to severe overfitting on the classification task. Therefore, we propose a systematic solution, termed Consistent-Teacher, to reduce the inconsistency. First, adaptive anchor assignment~(ASA) substitutes the static IoU-based strategy, which enables the student network to be resistant to noisy pseudo bounding boxes; Then we calibrate the subtask predictions by designing a 3D feature alignment module~(FAM-3D). It allows each classification feature to adaptively query the optimal feature vector for the regression task at arbitrary scales and locations. Lastly, a Gaussian Mixture Model (GMM) dynamically revises the score threshold of the pseudo-bboxes, which stabilizes the number of ground-truths at an early stage and remedies the unreliable supervision signal during training. Consistent-Teacher provides strong results on a large range of SSOD evaluations. It achieves 40.0 mAP with ResNet-50 backbone given only 10\% of annotated MS-COCO data, which surpasses previous baselines using pseudo labels by around 3 mAP. When trained on fully annotated MS-COCO with additional unlabeled data, the performance further increases to 47.2 mAP. Our code will be open-sourced soon

Transcutaneous auricular vagus nerve stimulation on upper limb motor function with stroke: a functional near-infrared spectroscopy pilot study

BackgroundTranscutaneous auricular vagus nerve stimulation (taVNS) emerges as a promising neuromodulatory technique. However, taVNS uses left ear stimulation in stroke survivors with either left or right hemiparesis. Understanding its influence on the cortical responses is pivotal for optimizing post-stroke rehabilitation protocols.ObjectiveThe primary objective of this study was to elucidate the influence of taVNS on cortical responses in stroke patients presenting with either left or right hemiparesis and to discern its potential ramifications for upper limb rehabilitative processes.MethodsWe employed functional near-infrared spectroscopy (fNIRS) to ascertain patterns of cerebral activation in stroke patients as they engaged in a “block transfer” task. Additionally, the Lateralization Index (LI) was utilized to quantify the lateralization dynamics of cerebral functions.ResultsIn patients exhibiting left-side hemiplegia, there was a notable increase in activation within the pre-motor and supplementary motor cortex (PMC-SMC) of the unaffected hemisphere as well as in the left Broca area. Conversely, those with right-side hemiplegia displayed heightened activation in the affected primary somatosensory cortex (PSC) region following treatment.Significantly, taVNS markedly amplified cerebral activation, with a pronounced impact on the left motor cortical network across both cohorts. Intriguingly, the LI showcased consistency, suggesting a harmonized enhancement across both compromised and uncompromised cerebral regions.ConclusionTaVNS can significantly bolster the activation within compromised cerebral territories, particularly within the left motor cortical domain, without destabilizing cerebral lateralization. TaVNS could play a pivotal role in enhancing upper limb functional restoration post-stroke through precise neuromodulatory and neuroplastic interventions

Effects of heat treatment on the microstructure of amorphous boron carbide coating deposited on graphite substrates by chemical vapor deposition

A two-layer boron carbide coating is deposited on a graphite substrate by chemical vapor deposition from a CH4/BCl3/H-2 precursor mixture at a low temperature of 950 degrees C and a reduced pressure of 10 KPa. Coated substrates are annealed at 1600 degrees C, 1700 degrees C, 1800 degrees C, 1900 degrees C and 2000 degrees C in high purity argon for 2 h, respectively. Structural evolution of the coatings is explored by electron microscopy and spectroscopy. Results demonstrate that the as-deposited coating is composed of pyrolytic carbon and amorphous boron carbide. A composition gradient of B and C is induced in each deposition. After annealing, B4C crystallites precipitate out of the amorphous boron carbide and grow to several hundreds nanometers by receiving B and C from boron-doped pyrolytic carbon. Energy-dispersive spectroscopy proves that the crystallization is controlled by element diffusion activated by high temperature annealing, after that a larger concentration gradient of B and C is induced in the coating. Quantified Raman spectrum identifies a graphitization enhancement of pyrolytic carbon. Transmission electron microscopy exhibits an epitaxial growth of B4C at layer/layer interface of the annealed coatings. Mechanism concerning the structural evolution on the basis of the experimental results is proposed. (C) 2010 Elsevier B.V. All rights reserved.National Natural Science Foundation of China [50532010, 90405015