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. The method's effectiveness was scrutinized using three datasets, exhibiting noteworthy improvements over both exacting and non-learning-based methods in terms of Dice score and Hausdorff distance.
We tackle the issue of image segmentation, which seeks to create a mask for the object described in a natural language statement. Recent works often incorporate Transformers to obtain object features by aggregating the attended visual regions, thereby aiding in the identification of the target. Yet, the generalized attention mechanism inherent in the Transformer architecture utilizes solely the language input for calculating attention weights, without explicitly incorporating linguistic features into the output. Consequently, visual data heavily influences its output, restricting the model's ability to grasp multifaceted information completely, which introduces uncertainty into the subsequent mask decoder's output mask extraction process. To rectify this issue, we propose the use of Multi-Modal Mutual Attention (M3Att) and Multi-Modal Mutual Decoder (M3Dec), thereby enhancing the merging of information from the two input modalities. Employing M3Dec as a foundation, we present Iterative Multi-modal Interaction (IMI) to enable sustained and in-depth communication between language and visual data. To avert the loss or misrepresentation of language information in the extracted features, we introduce Language Feature Reconstruction (LFR). The RefCOCO datasets consistently reveal that our proposed approach yields a substantial improvement over the baseline, outperforming leading-edge referring image segmentation methods in extensive experiments.
Salient object detection (SOD) and camouflaged object detection (COD) tasks are demonstrably typical within the realm of object segmentation. Their apparent contradiction belies their inherent connection. This paper examines the relationship between SOD and COD, utilizing successful SOD models for the detection of camouflaged objects to reduce the development cost associated with COD models. The foremost understanding is that both SOD and COD harness two facets of information object semantic representations to distinguish objects from the background, and context-based attributes that specify the category of the object. To begin, a novel decoupling framework, incorporating triple measure constraints, is used to separate context attributes and object semantic representations from the SOD and COD datasets. Via an attribute transfer network, saliency context attributes are then conveyed to the camouflaged images. 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. Rigorous experiments conducted on three popular COD datasets affirm the capability of the introduced method. Access the code and model at the following link: https://github.com/wdzhao123/SAT.
Outdoor visual imagery frequently suffers from degradation in the presence of thick smoke or haze. (R)-HTS-3 cell line Researching scene understanding in degraded visual environments (DVE) faces a critical hurdle: the absence of comprehensive benchmark datasets. To evaluate the state-of-the-art object recognition and other computer vision algorithms in adverse conditions, these datasets are imperative. 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. Employing professional smoke-generating machines to fully cover the scene within a controlled environment, this dataset was generated. Images were captured from the perspectives of both an unmanned aerial vehicle (UAV) and an unmanned ground vehicle (UGV). We further evaluate a series of representative, cutting-edge dehazing methodologies, alongside object identification models, using the provided dataset. For the community's use in evaluating algorithms, the complete dataset from this paper is available online. It includes ground truth object classification bounding boxes and haze density measurements at https//a2i2-archangel.vision. A part of this dataset was selected for the CVPR UG2 2022 challenge's Object Detection task in the Haze Track, accessible through https://cvpr2022.ug2challenge.org/track1.html.
Virtual reality systems and smartphones, among other everyday devices, employ vibration feedback as a common feature. However, engagement in mental and physical tasks could potentially obstruct our perception of vibrations from devices. This study constructs and analyzes a smartphone application to investigate how shape-memory tasks (cognitive activities) and walking (physical activities) diminish the perceived strength of smartphone vibrations. Our research investigated the effects of Apple's Core Haptics Framework parameters on haptics research, with a particular focus on the correlation between hapticIntensity and the amplitude of 230 Hz vibrations. A 23-person user study investigated the impact of physical and cognitive activity on vibration perception thresholds, revealing a significant effect (p=0.0004). Vibrations are perceived more swiftly when cognitive engagement is heightened. Furthermore, this study presents a smartphone application for vibration perception assessment in non-laboratory environments. By leveraging our smartphone platform and the results it generates, researchers can develop superior haptic devices specifically designed for diverse and unique user populations.
Along with the booming virtual reality application sector, a significant need persists for technological solutions to engender convincing self-motion, offering a less burdensome alternative to the substantial and cumbersome machinery of motion platforms. Haptic devices, centered on the sense of touch, have seen researchers increasingly adept at targeting the sense of motion through precise and localized haptic stimulations. Haptic motion, a specific paradigm, is exemplified by this innovative approach. This article's purpose is to introduce, formalize, survey, and discuss the relatively recent field of study. Initially, we outline key concepts related to self-motion perception, and then offer a definition of the haptic motion approach, grounded in three distinct criteria. We now present a comprehensive summary of existing related research, from which three pivotal research issues are formulated and analyzed: designing a proper haptic stimulus, assessing and characterizing self-motion sensations, and implementing multimodal motion cues.
Medical image segmentation is investigated in this study through a barely-supervised technique, employing a scarce dataset of labeled data, consisting of only single-digit cases. Translational Research A noteworthy constraint within contemporary semi-supervised approaches, especially cross pseudo-supervision, is the unsatisfactory precision assigned to foreground classes. This imprecision ultimately degrades the results in scenarios with minimal supervision. In this document, we detail a novel strategy, Compete-to-Win (ComWin), for enhancing pseudo-label accuracy. Our approach diverges from using a single model's predictions as pseudo-labels; instead, we generate high-quality pseudo-labels by comparing the confidence maps of various networks and selecting the most confident output (a win-through comparison strategy). 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. Evaluated on three public medical datasets concerning cardiac structure segmentation, pancreas segmentation, and colon tumor segmentation, our methodology demonstrates superior results compared to alternative approaches. Biogenic resource The source code, part of the comwin project, is now downloadable from the GitHub link https://github.com/Huiimin5/comwin.
Binary dithering, a hallmark of traditional halftoning, often sacrifices color fidelity when rendering images with discrete dots, thereby hindering the retrieval of the original color palette. A novel halftoning technique, capable of converting a color image to a binary halftone with complete restorability to its original form, was developed. Two convolutional neural networks (CNNs), central to our novel halftoning base method, create reversible halftone patterns, with a noise incentive block (NIB) further mitigating the flatness degradation issue frequently observed in CNN halftoning applications. The conflict between blue-noise quality and restoration precision in our novel baseline approach was tackled by a predictor-embedded methodology. This approach detaches predictable network data—the luminance information mirroring the halftone pattern. The network's capacity for producing halftones with improved blue-noise characteristics is increased by this strategy, without sacrificing the restoration's quality. Detailed research on the multiple-stage training approach and the weightings applied to various loss functions has been undertaken. Our predictor-embedded methodology and a novel technique were benchmarked against each other in the context of spectrum analysis on halftones, evaluating halftone fidelity, accuracy of restoration, and data embedding experiments. Our halftone's encoding information content, as determined by entropy evaluation, proves to be lower than that of our innovative base method. Experimental results confirm our predictor-embedded method's improved flexibility in enhancing the blue-noise quality of halftones, retaining comparable restoration quality while exhibiting higher tolerance levels for disturbances.
3D dense captioning, by semantically describing each detected 3D object within a scene, plays a critical part in scene interpretation. 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.