GSF employs grouped spatial gating to dissect the input tensor, subsequently combining the segmented tensors using channel-wise fusion. GSF seamlessly integrates with existing 2D CNNs, resulting in an efficient and high-performing spatio-temporal feature extractor with an insignificant impact on parameters and computational complexity. We conduct a comprehensive analysis of GSF, utilizing two prevalent 2D CNN architectures, achieving top-tier or comparable performance on five standard benchmarks for action recognition.

Inferencing with embedded machine learning models at the edge necessitates a careful consideration of the trade-offs between resource metrics like energy and memory usage and performance metrics like processing speed and prediction accuracy. Our research surpasses traditional neural network methods, investigating the Tsetlin Machine (TM), an emerging machine learning algorithm. This approach employs learning automata to formulate propositional logic rules for classification. selleck products Our novel methodology for TM training and inference utilizes the principles of algorithm-hardware co-design. The REDRESS method, composed of independent training and inference steps for transition machines, aims to reduce the memory requirements of the resulting automaton, targeting applications needing low and ultra-low power consumption. In the Tsetlin Automata (TA) array, learned data is represented in binary form, with bits 0 denoting excludes and bits 1 denoting includes. REDRESS's include-encoding, a lossless TA compression approach, achieves over 99% compression by only storing information regarding inclusion elements. Augmented biofeedback The Tsetlin Automata Re-profiling method, a computationally minimal training procedure, is employed to improve the accuracy and sparsity of TAs, thereby reducing the number of inclusions and, consequently, the memory footprint. Finally, REDRESS's inference algorithm, intrinsically bit-parallel, operates on the optimized TA in its compressed form, ensuring no decompression is needed during runtime, resulting in superior speedups when contrasted with 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 tasks often incorporate the utilization of datasets such as MNIST, CIFAR2, KWS6, Fashion-MNIST, and Kuzushiji-MNIST. By employing REDRESS on the STM32F746G-DISCO microcontroller, substantial speedups and energy savings were observed, ranging from 5 to 5700 times better than using competing BNN models.

Deep learning's impact on image fusion tasks is evident through the promising performance of fusion methods. The fusion process exhibits this characteristic because the network architecture plays a very important role. Even though a strong fusion architecture is hard to determine, this consequently means that designing fusion networks is more akin to a craft than a science. For the purpose of resolving this problem, we formulate the fusion task mathematically and demonstrate the correlation between its optimal outcome and the network architecture that facilitates its implementation. This approach underpins a novel method for constructing a lightweight fusion network, as detailed in the paper. Instead of the laborious and time-consuming empirical approach to network design, which relies on testing, it presents a different and more effective strategy. Specifically, we employ a learnable representation method for the fusion process, where the fusion network's architectural design is influenced by the optimization algorithm shaping the learned model. Our learnable model's foundation rests on the low-rank representation (LRR) objective. Convolutional operations are substituted for the matrix multiplications, the heart of the solution, and the iterative optimization process is replaced with a unique feed-forward network. From this pioneering network architecture, an end-to-end, lightweight fusion network is built, aiming to combine infrared and visible light images. The function that facilitates its successful training is a detail-to-semantic information loss function, carefully constructed to retain image details and enhance the essential features of the source images. Experiments performed on public datasets show that the proposed fusion network achieves superior fusion performance relative to the prevailing state-of-the-art fusion methods. It's intriguing that our network needs fewer training parameters than other current methods.

Deep models for visual recognition face a significant hurdle in learning from long-tailed datasets, requiring the training of robust deep architectures on a large number of images following this distribution. The last ten years have witnessed the emergence of deep learning as a formidable recognition model, facilitating the learning of high-quality image representations and producing remarkable progress in generic visual recognition. Nonetheless, the problem of class imbalance, a frequent challenge in real-world visual recognition tasks, frequently limits the usability of deep learning-based recognition models, as these models tend to be biased towards the more common classes and underperform on less prevalent classes. To combat this issue, a significant number of studies have been performed recently, yielding positive outcomes in the area of deep long-tailed learning. In light of the field's rapid evolution, this paper seeks to offer a comprehensive survey of recent innovations in deep long-tailed learning. In detail, we group existing deep long-tailed learning studies under three key categories: class re-balancing, information augmentation, and module improvement. We will analyze these approaches methodically within this framework. We then empirically investigate several leading-edge methods, scrutinizing their handling of class imbalance based on a newly proposed evaluation metric: relative accuracy. tethered membranes In the concluding section of the survey, we spotlight the critical applications of deep long-tailed learning and identify some exciting prospective research directions.

Diverse connections exist between objects within a singular scene, but only a small selection of these relationships are noteworthy. We, being influenced by the Detection Transformer's exceptional performance in object detection, regard scene graph generation as a problem in predicting sets. We propose Relation Transformer (RelTR), an end-to-end scene graph generation model, built with an encoder-decoder structure within this paper. While the encoder examines the visual feature context, the decoder, through the application of various attention mechanisms, deduces a fixed-size collection of subject-predicate-object triplets, coupling subject and object queries. For our end-to-end training framework, a set prediction loss is developed to ensure the accurate correspondence between predicted and ground truth triplets. RelTR's one-step methodology diverges from other scene graph generation methods by directly predicting sparse scene graphs using only visual cues, eschewing entity aggregation and the annotation of all possible relationships. Our model demonstrates superior performance and rapid inference, as evidenced by extensive experiments on the Visual Genome, Open Images V6, and VRD datasets.

Local feature detection and description are essential components in many vision applications, driven by strong industrial and commercial applications. In substantial applications, these undertakings demand exacting standards for both the precision and swiftness of local characteristics. Existing studies on local feature learning often concentrate on the descriptions of individual keypoints, overlooking the connections these keypoints have based on an overall spatial understanding. Employing a consistent attention mechanism (CoAM), AWDesc, as presented in this paper, facilitates local descriptor awareness of image-level spatial context, both during training and matching. Local feature detection, enhanced by a feature pyramid, is employed to achieve more stable and accurate localization of keypoints. Addressing varying needs for accuracy and speed in describing local features, we offer two versions of AWDesc. We introduce Context Augmentation to overcome the inherent locality of convolutional neural networks, enriching local descriptors with non-local contextual information for more comprehensive descriptions. Specifically, to build robust local descriptors incorporating context from global to surrounding areas, we propose the well-designed Adaptive Global Context Augmented Module (AGCA) and the Diverse Surrounding Context Augmented Module (DSCA). Alternatively, we craft a remarkably lightweight backbone network, incorporating a custom knowledge distillation approach, for the optimal combination of accuracy and speed. In addition, we execute extensive experiments on image matching, homography estimation, visual localization, and 3D reconstruction tasks, and the results clearly indicate that our method exhibits superiority over current state-of-the-art local descriptors. The AWDesc code is readily downloadable from the GitHub link https//github.com/vignywang/AWDesc.

Accurate matching of points within point clouds is essential for tasks like 3D registration and recognition. This document details a mutual voting technique for establishing the order of 3D correspondences. Refining both the pool of voters and the pool of candidates is integral to achieving reliable scoring for correspondences within a mutual voting system. A graph is formulated from the initial correspondence set, with the pairwise compatibility rule as a guiding principle. Nodal clustering coefficients are introduced in the second instance to provisionally eliminate a fraction of outliers, thereby hastening the subsequent voting phase. In our third model, nodes are treated as candidates, and edges as the corresponding voters. To evaluate the correspondences, mutual voting takes place within the graph's structure. The final step involves ranking the correspondences by their voting scores, and the top-ranked correspondences are then identified as inliers.