To accomplish a physically plausible transformation, diffeomorphisms are used to determine the transformations and activation functions, which are designed to constrain the range of radial and rotational components. Over three different data sets, the method displayed a noteworthy improvement in Dice score and Hausdorff distance, exceeding the performance of both exacting and non-learning-based techniques.
We investigate the problem of image segmentation, with the goal of producing a mask for the object identified through a natural language description. Transformer models are increasingly employed in recent research to extract features of the target object by combining attended visual segments. However, the standard attention mechanism within a Transformer model utilizes only language input for calculating attention weights, failing to explicitly combine language features in the output. Therefore, its output is predominantly determined by visual inputs, thus hindering a full understanding of the combined modalities, leading to ambiguity in the subsequent mask decoder's mask generation. For the purpose of addressing this issue, we present Multi-Modal Mutual Attention (M3Att) and Multi-Modal Mutual Decoder (M3Dec), methods that more comprehensively combine data from the two input types. Drawing from M3Dec, we develop Iterative Multi-modal Interaction (IMI) for facilitating ongoing and detailed interactions between language and vision information. Subsequently, a language feature reconstruction mechanism (LFR) is implemented to ensure that the extracted features faithfully represent the language information, preventing any potential loss or corruption. Our proposed approach consistently shows a significant advancement over the baseline, outperforming state-of-the-art referring image segmentation methods on the RefCOCO dataset series in extensive trials.
Object segmentation tasks, such as salient object detection (SOD) and camouflaged object detection (COD), are quite typical. Their apparent contradiction belies their inherent connection. In this paper, we investigate the relationship between SOD and COD, then borrowing from successful SOD model designs to detect hidden objects, thus reducing the cost of developing COD models. The crucial insight reveals that both SOD and COD draw upon two dimensions of information object semantic representations to delineate objects from backgrounds, and contextual attributes that determine object categories. Our initial step involves separating context attributes and object semantic representations from SOD and COD datasets, facilitated by a newly devised decoupling framework that incorporates triple measure constraints. Subsequently, saliency context attributes are transferred to the camouflaged images by way of an attribute transfer network. The creation of images with weak camouflage allows bridging the contextual attribute gap between Source Object Detection and Contextual Object Detection, improving the performance of Source Object Detection models on Contextual Object Detection datasets. Systematic investigations on three commonly-encountered COD datasets corroborate the effectiveness of the introduced approach. The code and model can be found at https://github.com/wdzhao123/SAT.
Dense smoke or haze often causes a decline in the quality of captured outdoor visual imagery. reuse of medicines Scene understanding research in degraded visual environments (DVE) is hindered by the dearth of representative benchmark datasets. In order to evaluate the most advanced object recognition and other computer vision algorithms in degraded circumstances, these datasets are necessary. We introduce, in this paper, a first realistic haze image benchmark, incorporating paired haze-free images, in-situ haze density measurements, and perspectives from both aerial and ground viewpoints, thus mitigating some of the existing limitations. In a controlled environment, the deployment of professional smoke-generating machines that covered the entire scene, led to the creation of this dataset of images. Images were captured from both an unmanned aerial vehicle (UAV) and an unmanned ground vehicle (UGV). Moreover, we assess a portfolio of advanced dehazing techniques and object detection systems on the given dataset. The dataset presented in this paper, containing ground truth object classification bounding boxes and haze density measurements, is accessible to the community for evaluating their algorithms at https//a2i2-archangel.vision. This dataset's selected portion was used in the Object Detection task of the CVPR UG2 2022 challenge's Haze Track; further information is available at https://cvpr2022.ug2challenge.org/track1.html.
In the realm of everyday devices, from smartphones to virtual reality systems, vibration feedback is a standard feature. Nevertheless, cognitive and physical endeavors might hinder our capacity to detect vibrations emitted by devices. This research project constructs and details a smartphone-based system to analyze how shape-memory tasks (mental activities) and walking (physical movements) influence how well people sense smartphone vibrations. This study scrutinized Apple's Core Haptics Framework parameters for their application in haptics research, particularly the effect of hapticIntensity on the amplitude of vibrations with a frequency of 230 Hz. A study of 23 individuals showed that participating in physical and cognitive activities led to a rise in the vibration perception thresholds (p=0.0004). Increased cognitive activity correlates with a decreased vibration response time. In addition, a smartphone platform designed for vibration perception testing is introduced in this work, allowing for evaluations outside the laboratory. By leveraging our smartphone platform and the results it generates, researchers can develop superior haptic devices specifically designed for diverse and unique user populations.
As virtual reality applications see expansion, the need for technological solutions to induce compelling self-motion intensifies, providing a more adaptable and streamlined alternative to the existing, cumbersome motion platforms. Although haptic devices primarily stimulate the sense of touch, recent research has successfully employed localized haptic input to also convey a sense of motion. This novel approach, which establishes a particular paradigm, is identified as 'haptic motion'. This article undertakes a comprehensive introduction, formalization, survey, and discussion of this emerging research area. To begin, we present core ideas regarding self-motion perception, and subsequently introduce a definition for the haptic motion approach, built on three defining characteristics. Following a review of the relevant existing literature, we identify and examine three critical research issues crucial to the field's development: developing the rationale for a suitable haptic stimulus design, evaluating and characterizing self-motion sensations, and the application of multimodal motion cues.
The current study examines medical image segmentation under a barely-supervised paradigm, constrained by the availability of only a handful of labeled examples, that is, less than ten labeled instances. intensive lifestyle medicine A key shortcoming of current semi-supervised methods, especially those utilizing cross pseudo-supervision, is the inadequate accuracy of foreground class identification. This inadequacy precipitates degraded performance in barely supervised learning situations. In this document, we detail a novel strategy, Compete-to-Win (ComWin), for enhancing pseudo-label accuracy. Differing from the direct use of a single model's predictions as pseudo-labels, our method generates high-quality pseudo-labels by comparing the confidence maps from various networks to determine the prediction with the greatest confidence (a competition-for-accuracy method). A boundary-aware improvement module is integrated into ComWin to create ComWin+, an enhanced version of the original algorithm for more accurate refinement of pseudo-labels near boundary zones. Across three public medical image datasets, specifically those focusing on cardiac structure, pancreas, and colon tumor segmentation, our approach exhibits optimal performance. BEZ235 concentration Please find the source code readily available at the given GitHub address, https://github.com/Huiimin5/comwin.
In traditional halftoning, the use of binary dots for dithering images typically leads to the loss of color information, thereby obstructing the accurate reconstruction of the original color details. We developed a novel halftoning technique for converting color images into binary halftones, with the capability of fully recovering the original picture. Two convolutional neural networks (CNNs) form the core of our novel halftoning base method, creating reversible halftone images. A noise incentive block (NIB) is integrated to address the flatness degradation problem frequently associated with CNN halftoning. In our novel base method, we encountered conflicts between blue-noise quality and restoration accuracy. To resolve this, we implemented a predictor-embedded approach to externalize predictable data from the network—luminance information mirroring the halftone pattern. Implementing this method empowers the network to achieve greater adaptability in producing halftones of improved blue-noise quality, all while maintaining the standard of the restoration. Thorough research into the multi-stage training method and the corresponding adjustments to loss function weights has been accomplished. Spectrum analysis on halftone imagery, halftone precision, restoration accuracy, and data embedding explorations served as the basis for comparing our predictor-embedded method and our innovative approach. Based on our entropy evaluation, the encoding information within our halftone is demonstrably smaller than in our novel baseline method. By means of experimentation, the efficacy of our predictor-embedded methodology in granting increased flexibility for improving halftone blue-noise quality and maintaining comparable restoration quality, despite heightened disturbances, is demonstrably validated.
Each 3D object detected in a scene is semantically described by 3D dense captioning, which significantly aids in 3D scene understanding. Existing research has not fully articulated 3D spatial relationships, nor has it effectively linked visual and linguistic representations, neglecting the disparities between these distinct modalities.
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