By employing grouped spatial gating, GSF dissects the input tensor, and afterward combines the segmented tensors through channel weighting. 2D CNNs can be augmented with GSF to function as highly efficient spatio-temporal feature extractors with an insignificant increase in parameters and computational load. Employing two prominent 2D CNN families, we perform a thorough analysis of GSF and obtain state-of-the-art or competitive performance across five standard action recognition benchmarks.

Resource metrics, including energy and memory, and performance metrics, including computation time and accuracy, present significant trade-offs when performing inference at the edge with embedded machine learning models. Departing from traditional neural network approaches, this work investigates Tsetlin Machines (TM), a rapidly developing machine learning algorithm. The algorithm utilizes learning automata to formulate propositional logic rules for classification. medication abortion Our novel methodology for TM training and inference utilizes the principles of algorithm-hardware co-design. Independent transition machine training and inference, incorporated in the REDRESS methodology, serve to minimize the memory footprint of the resulting automata, particularly for low and ultra-low power applications. The Tsetlin Automata (TA) array contains learned data, encoded as binary bits 0 and 1, distinguishing between excludes and includes. REDRESS's include-encoding, a lossless TA compression approach, achieves over 99% compression by only storing information regarding inclusion elements. Hereditary PAH TAs' accuracy and sparsity are improved by a novel, computationally economical training method, the Tsetlin Automata Re-profiling technique. This reduces the inclusion count, thereby lessening the memory footprint. REDRESS's bit-parallel inference algorithm, applied to the optimally trained TA within the compressed domain, efficiently avoids decompression during runtime, ultimately yielding significant speed enhancements over state-of-the-art Binary Neural Network (BNN) models. Using the REDRESS methodology, TM models achieve superior performance relative to BNN models on all design metrics, validated across five benchmark datasets. Machine learning research frequently utilizes the datasets MNIST, CIFAR2, KWS6, Fashion-MNIST, and Kuzushiji-MNIST. REDRESS, when executed on the STM32F746G-DISCO microcontroller, showcased speed and energy efficiency gains between 5 and 5700 compared to competing BNN architectures.

Image fusion tasks have seen encouraging results thanks to fusion methods built upon deep learning principles. The fusion process's success is directly attributable to the significance of the network architecture. Nonetheless, pinpointing an ideal fusion architecture proves challenging, and as a result, the design of fusion networks remains an arcane practice, rather than a methodical science. In order to resolve this predicament, we mathematically define the fusion task, and establish a correspondence between its optimal resolution and the network architecture that can enact it. This approach results in the creation of a novel, lightweight fusion network, as outlined in the paper's method. It circumvents the laborious empirical network design process, which relies on a trial-and-error approach. To address the fusion task, we implement a learnable representation technique. The optimization algorithm creating the learnable model also guides the fusion network's construction. Our learnable model's foundation rests on the low-rank representation (LRR) objective. By replacing the iterative optimization process with a specialized feed-forward network, the matrix multiplications, central to the solution, are transformed into convolutional operations. Employing this novel network design, a lightweight, end-to-end fusion network is created, merging infrared and visible light imagery. The successful training of this model is made possible by a detail-to-semantic information loss function that is intended to retain image details and highlight the salient characteristics of the source images. Our experiments demonstrate that the proposed fusion network surpasses the current leading fusion methods in terms of fusion performance, as evaluated on publicly available datasets. It is noteworthy that our network necessitates fewer training parameters compared to other existing methodologies.

To address long-tailed distributions in visual recognition, deep long-tailed learning aims to train high-performing deep models on massive image datasets reflecting this class distribution. The last decade has seen deep learning become a significant recognition model for acquiring high-quality image representations and achieving remarkable advancements in the broad field of visual recognition. Nevertheless, the problematic class imbalance, a common occurrence in visual recognition tasks, frequently hinders the applicability of deep learning-based recognition models in practical situations, since these models frequently exhibit a bias toward prominent classes and perform poorly on less frequent ones. A plethora of studies have been performed in recent years to address this concern, showcasing encouraging strides in the field of deep long-tailed learning. In view of the significant evolution within this field, this paper is dedicated to providing an extensive survey of recent achievements in deep long-tailed learning. Explicitly, we sort existing deep long-tailed learning studies into three fundamental categories: class re-balancing, information augmentation, and module refinement. We will examine these approaches in detail, using this organizational structure. Our empirical analysis of multiple state-of-the-art methods follows, evaluating their capacity to address class imbalance using a newly proposed metric, namely relative accuracy. Selleck TPEN The survey wraps up by emphasizing the key applications of deep long-tailed learning and identifying compelling future research directions.

In any given scene, the connections between various objects vary in strength, with only a select few relationships standing out. Influenced by the Detection Transformer's proficiency in object detection, we frame scene graph generation as a problem concerning set prediction. This paper introduces Relation Transformer (RelTR), an end-to-end scene graph generation model employing an encoder-decoder structure. The encoder analyzes the visual feature context, and the decoder uses various attention mechanisms to infer a fixed-size set of subject-predicate-object triplets, employing coupled subject and object queries. In the context of end-to-end training, a set prediction loss is constructed for the purpose of aligning predicted triplets with their respective ground truth values. Unlike the majority of existing scene graph generation approaches, RelTR employs a single-stage architecture, directly forecasting sparse scene graphs based solely on visual cues without integrating entities or annotating every potential predicate. Experiments across the Visual Genome, Open Images V6, and VRD datasets highlight our model's quick inference and superior performance.

The detection and description of local features remain essential in numerous vision applications, driving high industrial and commercial activity. Local features, in large-scale applications, are expected to exhibit both high accuracy and rapid processing speed, given the tasks involved. Many studies of local features learning are fixated on the individual characteristics of detected keypoints, while neglecting the spatial relationships they implicitly form through global awareness. The consistent attention mechanism (CoAM), central to AWDesc presented in this paper, enables local descriptors to encompass image-level spatial context, both during training and during matching. By using a feature pyramid in combination with local feature detection, more stable and accurate keypoint localization can be achieved. In describing local features, two variants of AWDesc are available to address the diverse needs of precision and speed. In order to address the inherent locality of convolutional neural networks, Context Augmentation injects non-local contextual information, which allows local descriptors to have a wider reach and provide more comprehensive descriptions. Employing context information from the surrounding and global regions, the Adaptive Global Context Augmented Module (AGCA) and the Diverse Surrounding Context Augmented Module (DSCA) are proposed to create robust local descriptors. Alternatively, we create a highly efficient backbone network structure, integrated with the custom knowledge distillation strategy, to attain the best compromise between speed and accuracy. Our experiments on image matching, homography estimation, visual localization, and 3D reconstruction procedures clearly demonstrate that our approach achieves superior results compared to the prevailing state-of-the-art local descriptors. On the platform GitHub, the project AWDesc has its code accessible at https//github.com/vignywang/AWDesc.

The consistent matching of points from different point clouds is a vital prerequisite for 3D vision tasks, including registration and object recognition. We describe, in this paper, a mutual voting system for the ranking of 3D correspondences. Achieving reliable scoring for correspondences in a mutual voting system hinges on refining both the voters and the candidates. A graph is generated using the initial correspondence set and applying the pairwise compatibility restriction. The second phase involves introducing nodal clustering coefficients to preemptively isolate and eliminate a group of outliers, thereby accelerating the subsequent voting procedure. Graph edges are treated as voters, and nodes as candidates, within our third model. Within the graph, mutual voting is employed to ascertain the score of correspondences. In the end, the correspondences are ranked based on the numerical value of their voting scores; the highest-scoring ones qualify as inliers.