ComPtr: Towards Diverse Bi-source Dense Prediction Tasks via A Simple yet General Complementary Transformer

Deep learning (DL) has advanced the field of dense prediction, while gradually dissolving the inherent barriers between different tasks. However, most existing works focus on designing architectures and constructing visual cues only for the specific task, which ignores the potential uniformity introduced by the DL paradigm. In this paper, we attempt to construct a novel \underline{ComP}lementary \underline{tr}ansformer, \textbf{ComPtr}, for diverse bi-source dense prediction tasks. Specifically, unlike existing methods that over-specialize in a single task or a subset of tasks, ComPtr starts from the more general concept of bi-source dense prediction. Based on the basic dependence on information complementarity, we propose consistency enhancement and difference awareness components with which ComPtr can evacuate and collect important visual semantic cues from different image sources for diverse tasks, respectively. ComPtr treats different inputs equally and builds an efficient dense interaction model in the form of sequence-to-sequence on top of the transformer. This task-generic design provides a smooth foundation for constructing the unified model that can simultaneously deal with various bi-source information. In extensive experiments across several representative vision tasks, i.e. remote sensing change detection, RGB-T crowd counting, RGB-D/T salient object detection, and RGB-D semantic segmentation, the proposed method consistently obtains favorable performance. The code will be available at \url{https://github.com/lartpang/ComPtr}

Multi-scale Interactive Network for Salient Object Detection

Deep-learning based salient object detection methods achieve great progress. However, the variable scale and unknown category of salient objects are great challenges all the time. These are closely related to the utilization of multi-level and multi-scale features. In this paper, we propose the aggregate interaction modules to integrate the features from adjacent levels, in which less noise is introduced because of only using small up-/down-sampling rates. To obtain more efficient multi-scale features from the integrated features, the self-interaction modules are embedded in each decoder unit. Besides, the class imbalance issue caused by the scale variation weakens the effect of the binary cross entropy loss and results in the spatial inconsistency of the predictions. Therefore, we exploit the consistency-enhanced loss to highlight the fore-/back-ground difference and preserve the intra-class consistency. Experimental results on five benchmark datasets demonstrate that the proposed method without any post-processing performs favorably against 23 state-of-the-art approaches. The source code will be publicly available at https://github.com/lartpang/MINet.Comment: Accepted by CVPR 202

CAVER: Cross-Modal View-Mixed Transformer for Bi-Modal Salient Object Detection

Most of the existing bi-modal (RGB-D and RGB-T) salient object detection methods utilize the convolution operation and construct complex interweave fusion structures to achieve cross-modal information integration. The inherent local connectivity of the convolution operation constrains the performance of the convolution-based methods to a ceiling. In this work, we rethink these tasks from the perspective of global information alignment and transformation. Specifically, the proposed \underline{c}ross-mod\underline{a}l \underline{v}iew-mixed transform\underline{er} (CAVER) cascades several cross-modal integration units to construct a top-down transformer-based information propagation path. CAVER treats the multi-scale and multi-modal feature integration as a sequence-to-sequence context propagation and update process built on a novel view-mixed attention mechanism. Besides, considering the quadratic complexity w.r.t. the number of input tokens, we design a parameter-free patch-wise token re-embedding strategy to simplify operations. Extensive experimental results on RGB-D and RGB-T SOD datasets demonstrate that such a simple two-stream encoder-decoder framework can surpass recent state-of-the-art methods when it is equipped with the proposed components.Comment: Updated version, more flexible structure, better performanc

ZoomNeXt: A Unified Collaborative Pyramid Network for Camouflaged Object Detection

Recent camouflaged object detection (COD) attempts to segment objects visually blended into their surroundings, which is extremely complex and difficult in real-world scenarios. Apart from the high intrinsic similarity between camouflaged objects and their background, objects are usually diverse in scale, fuzzy in appearance, and even severely occluded. To this end, we propose an effective unified collaborative pyramid network which mimics human behavior when observing vague images and videos, \textit{i.e.}, zooming in and out. Specifically, our approach employs the zooming strategy to learn discriminative mixed-scale semantics by the multi-head scale integration and rich granularity perception units, which are designed to fully explore imperceptible clues between candidate objects and background surroundings. The former's intrinsic multi-head aggregation provides more diverse visual patterns. The latter's routing mechanism can effectively propagate inter-frame difference in spatiotemporal scenarios and adaptively ignore static representations. They provides a solid foundation for realizing a unified architecture for static and dynamic COD. Moreover, considering the uncertainty and ambiguity derived from indistinguishable textures, we construct a simple yet effective regularization, uncertainty awareness loss, to encourage predictions with higher confidence in candidate regions. Our highly task-friendly framework consistently outperforms existing state-of-the-art methods in image and video COD benchmarks. The code will be available at \url{https://github.com/lartpang/ZoomNeXt}.Comment: Extensions to the conference version: arXiv:2203.02688; Fixed some word error

Freeze-thaw damage assessment of engineered cementitious composites using the electrochemical impedance spectroscopy method

The mechanical properties of engineered cementitious composites (ECC) in service in cold regions can be significantly degraded by periodic freezing and thawing. In this work, the damage degree of freeze–thaw of ECC was systematically assessed by using the electrochemical impedance spectroscopy (EIS) technique. In addition, Nuclear Magnetic Resonance (NMR) Relaxometry measurements were also performed to obtain pore structure parameters, and the uniaxial tensile tests were also carried out to analyse the tensile performance after freeze–thaw cycles. From the acquired results, it was demonstrated that the EIS behaviour of ECC varied with the freeze–thaw cycles. The diameter of the Nyquist curve in high-frequency was gradually reduced by increasing the freeze–thaw cycles. Furthermore, the volume resistance of ECC after freeze–thaw gradually decreased with the increase in the number of freeze–thaw cycles. The simplified microstructure and conductive paths were used to describe the freeze–thaw damage mechanism of ECC. An equivalent circuit model of ECC exposed to freeze–thaw cycles was proposed, and the parameters of the equivalent circuit model were thoroughly analysed. The experimental findings clearly indicate that the EIS method is an appropriate technique for evaluating the damage degree of freeze–thaw of ECC

Emerging role of the calcium-activated, small conductance, SK3 K ⁺ channel in distal tubule function: Regulation by TRPV4

The Ca2+-activated, maxi-K (BK) K+ channel, with low Ca2+-binding affinity, is expressed in the distal tubule of the nephron and contributes to flow-dependent K+ secretion. In the present study we demonstrate that the Ca2+-activated, SK3 (KCa2.3) K + channel, with high Ca2+-binding affinity, is also expressed in the mouse kidney (RT-PCR, immunoblots). Immunohistochemical evaluations using tubule specific markers demonstrate significant expression of SK3 in the distal tubule and the entire collecting duct system, including the connecting tubule (CNT) and cortical collecting duct (CCD). In CNT and CCD, main sites for K+ secretion, the highest levels of expression were along the apical (luminal) cell membranes, including for both principal cells (PCs) and intercalated cells (ICs), posturing the channel for Ca2+- dependent K+ secretion. Fluorescent assessment of cell membrane potential in native, split-opened CCD, demonstrated that selective activation of the Ca2+-permeable TRPV4 channel, thereby inducing Ca2+ influx and elevating intracellular Ca2+ levels, activated both the SK3 channel and the BK channel leading to hyperpolarization of the cell membrane. The hyperpolarization response was decreased to a similar extent by either inhibition of SK3 channel with the selective SK antagonist, apamin, or by inhibition of the BK channel with the selective antagonist, iberiotoxin (IbTX). Addition of both inhibitors produced a further depolarization, indicating cooperative effects of the two channels on Vm. It is concluded that SK3 is functionally expressed in the distal nephron and collecting ducts where induction of TRPV4-mediated Ca2+ influx, leading to elevated intracellular Ca2+ levels, activates this high Ca2+- affinity K+ channel. Further, with sites of expression localized to the apical cell membrane, especially in the CNT and CCD, SK3 is poised to be a key pathway for Ca2+-dependent regulation of membrane potential and K+ secretion. © 2014 Berrout et al