On public datasets, extensive experiments were performed. The results indicated that the proposed methodology performed far better than existing leading-edge methods and matched the fully-supervised upper bound, demonstrating a 714% mIoU increase on GTA5 and a 718% mIoU increase on SYNTHIA. Thorough ablation studies also confirm the effectiveness of each component.

High-risk driving situations are often evaluated by estimating potential collisions or detecting recurring accident patterns. The problem is approached in this work with a focus on subjective risk. By foreseeing driver behavior changes and identifying the root of these changes, we operationalize subjective risk assessment. In this regard, we propose a new task, driver-centric risk object identification (DROID), that employs egocentric video to locate objects impacting a driver's behavior, solely guided by the driver's reaction. We approach the problem as a causal sequence, outlining a novel two-stage DROID framework motivated by models of situation comprehension and causal reasoning. Evaluation of DROID leverages a selected segment of the Honda Research Institute Driving Dataset (HDD). This dataset serves as a platform for demonstrating the advanced capabilities of our DROID model, whose performance exceeds that of strong baseline models. Besides this, we carry out in-depth ablative studies to corroborate our design decisions. In addition, we exemplify the practical use of DROID in risk assessment.

This paper contributes to the growing area of loss function learning, detailing the construction of loss functions that markedly improve model performance. Our new meta-learning framework, leveraging a hybrid neuro-symbolic search approach, enables the learning of model-agnostic loss functions. The framework's initial stage involves evolution-based searches within the space of primitive mathematical operations, yielding a set of symbolic loss functions. Hepatic alveolar echinococcosis Following learning, the loss functions are parameterized and optimized using an end-to-end gradient-based training approach. The proposed framework displays empirical versatility across a diverse spectrum of supervised learning tasks. BAY593 On a variety of neural network architectures and datasets, the meta-learned loss functions produced by this new method are more effective than both cross-entropy and current leading loss function learning techniques. The link to our code is now *retracted*.

Neural architecture search (NAS) has garnered substantial attention from researchers and practitioners in both academia and industry. The substantial search space and considerable computational costs continue to pose a significant challenge. Recent NAS research trends emphasize the repeated use of weight sharing mechanisms in a single training run of a SuperNet. Despite this, the corresponding subnetwork branch is not guaranteed to have completed its training process. The retraining process may entail not only significant computational expense but also a change in the ranking of the architectures. This paper proposes a multi-teacher-guided neural architecture search (NAS) algorithm, integrating an adaptive ensemble and perturbation-aware knowledge distillation technique for one-shot NAS. Adaptive coefficients for the feature maps within the combined teacher model are determined through an optimization method that seeks optimal descent directions. Furthermore, we suggest a particular knowledge distillation technique for both optimal and perturbed architectures within each search iteration to develop superior feature maps for subsequent distillation steps. Detailed empirical studies show our approach's flexibility and successful application. Within the standard recognition dataset, our system demonstrates superior precision and search efficiency. Improved correlation between the precision of the search algorithm and true accuracy is observed using the NAS benchmark datasets.

Globally distributed databases harbor billions of fingerprint images acquired by direct contact methods. Contactless 2D fingerprint identification systems have become highly sought after as a more hygienic and secure alternative during the current pandemic. To ensure the success of this alternative, precise matching is critical, spanning both contactless-to-contactless comparisons and the currently deficient contactless-to-contact-based pairings, failing to meet expectations for substantial-scale implementations. To advance match accuracy expectations and address privacy issues, including those defined by recent GDPR regulations, a novel methodology is presented for the acquisition of extremely large databases. This paper presents a novel methodology for the precise creation of multi-view contactless 3D fingerprints, enabling the development of a large-scale multi-view fingerprint database, alongside a complementary contact-based fingerprint database. A significant advantage of our technique is the simultaneous availability of indispensable ground truth labels, along with the reduction of the often error-prone and laborious human labeling process. In addition, a new framework is presented that achieves accurate matching between contactless and contact-based images, as well as between contactless images themselves. This dual capacity is crucial for the advancement of contactless fingerprint technology. Both within-database and cross-database experiments, as meticulously documented in this paper, yielded results that surpassed expectations and validated the efficacy of the proposed approach.

To explore the relationships between consecutive point clouds and determine the scene flow that indicates 3D motions, this paper proposes Point-Voxel Correlation Fields. Existing research often emphasizes local correlations, capable of handling minor movements, but failing to adequately address large displacements. Hence, incorporating all-pair correlation volumes, which transcend local neighbor constraints and encompass both short-term and long-term dependencies, is paramount. In contrast, the efficient derivation of correlation attributes from every point pair within a 3D framework is problematic, considering the random and unstructured structure of point clouds. Point-voxel correlation fields are introduced to address this problem, with unique point and voxel branches dedicated to the examination of local and long-range correlations from all-pair fields. The K-Nearest Neighbors approach is used to exploit point-based correlations, ensuring the preservation of fine-grained details within the local vicinity, thus guaranteeing accurate scene flow estimation. Through multi-scale voxelization of point clouds, we build pyramid correlation voxels, which represent long-range correspondences, allowing for effective handling of fast-moving objects. From point clouds, scene flow estimation is achieved using the iterative Point-Voxel Recurrent All-Pairs Field Transforms (PV-RAFT) architecture, which incorporates these two correlation types. To produce more granular results in dynamic flow environments, we developed DPV-RAFT, which employs spatial deformation to modify the voxelized neighborhood and temporal deformation to adjust the iterative process. Experimental results, obtained by applying our proposed method to the FlyingThings3D and KITTI Scene Flow 2015 datasets, demonstrate a substantial margin of superiority over existing state-of-the-art methods.

Local, single-source datasets have fostered the development of successful pancreas segmentation methods, which are achieving promising outcomes. These methodologies, though implemented, do not effectively consider the aspect of generalizability, and therefore typically demonstrate limited efficacy and low stability on test datasets from other contexts. Due to the restricted variety of data sources, we strive to improve the ability of a pancreas segmentation model, trained solely on one source, to generalize its performance; this embodies the single-source generalization problem. This work introduces a dual self-supervised learning model that incorporates both global and local anatomical contexts for analysis. The anatomical features within and outside the pancreas are fundamentally explored by our model to provide a more robust characterization of high-uncertainty regions, thus strengthening its generalization ability. Using the spatial layout of the pancreas as a guide, we initially develop a global feature contrastive self-supervised learning module. Complete and consistent pancreatic features are procured by this module through the enhancement of internal similarity within the class; this module concurrently extracts more distinctive characteristics for the differentiation of pancreatic from non-pancreatic tissues by optimizing the division between classes. This technique reduces the contribution of surrounding tissue to segmentation errors, especially in areas of high uncertainty. Following which, a self-supervised learning module for the restoration of local images is deployed to provide an enhanced characterization of high-uncertainty regions. In this module, the learning of informative anatomical contexts actually allows for the recovery of randomly corrupted appearance patterns within those regions. State-of-the-art performance and a comprehensive ablation analysis across three pancreatic datasets (467 cases) validate the efficacy of our methodology. The results demonstrate a significant potential to ensure dependable support for the diagnosis and care of pancreatic disorders.

Pathology imaging is standardly used to identify the underlying reasons and consequences of diseases or injuries. To enable computers to answer queries regarding clinical visual aspects from pathology images is the goal of the pathology visual question answering system, PathVQA. Waterproof flexible biosensor Existing PathVQA methodologies have relied on directly examining the image content using pre-trained encoders, omitting the use of beneficial external data when the image's substance was inadequate. We present K-PathVQA, a knowledge-driven PathVQA system in this paper, that utilizes a medical knowledge graph (KG) from a complementary external structured knowledge base for inferring answers to PathVQA questions.