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[Display omitted] • The microstructure and mechanical anisotropy of additively manufactured CoCrNi medium-entropy alloys were studied after HIP and UIT. • HIP induces the formation of large number of Cr 2 O 3 particles, and transformation of LAGBs into HAGBs. • UIT contributed to the increase in the number of LAGBs and induced the transformation of columnar grains to equiaxed grains, thus decreasing the anisotropy of AM CoCrNi MEAs. The anisotropy properties of samples in different direction is found in additively manufactured (AM) CoCrNi medium-entropy alloys (MEAs). In this study, the laser direct energy deposition AM CoCrNi MEAs have been subjected to two alternative processing methods: hot isostatic pressing (HIP) and ultrasonic impact treatment (UIT). The effect of HIP and UIT on the microstructure, grain orientation, grain boundary distribution, phase distribution and mechanical properties of AM CoCrNi MEAs were systematically studied. The results show that HIP decreased the partial mechanical anisotropy of AM CoCrNi MEAs, and YS was significantly reduced. The main reason is that the HIP induces the formation of large number of Cr 2 O 3 particles, and transformation of low-angle grain boundaries (LAGBs) into high-angle grain boundaries (HAGBs). Surprisingly, both the yield strength (YS) and ultimate tensile strength (UTS) of the AM CoCrNi MEAs were increased when the UIT was added after every laser deposition of one layer (UIT-1). This is due to the combined effect of quasi-static loading and ultrasonic oscillations of UIT, which results in the dislocation multiplication and the formation of a large number of substructures. In addition, the above strengthening phenomena also lead to decrease partial mechanical anisotropy of AM CoCrNi MEAs. [ABSTRACT FROM AUTHOR]

Visual behavior intention inference is crucial for enabling escort robots to interact naturally with humans, which is very challenging due to the big inner-class similarity and the small intra-class distinguishability of successive actions in the assistive scenario. To attain a reliable behavior intention inference, not only the current state of behaviors is concerned, but also the semantic information in both spatial and temporal domains plays an important role. This paper presents a segmentation–detection–recognition hierarchical system to represent the spatio-temporal semantic features for formulating descriptions of body parts, trajectories and deep relationships of sub-behaviors. Specifically, a dense trajectory matching scheme based on temporal sampling and Binarized Normed Gradients (BING) algorithm is formulated to segment the 3-Dimensional (3D) behavior cubes, based on which, local trajectories are obtained by clustering dense trajectories according to the distance similarity, and the body parts are then detected by multi-kernel learning of the encoded local features. Moreover, a global three-stream context Convolutional Neural Networks (CNN) is proposed for behavior classification by designing a texture module using expansion, connection and 1D convolution implementations. Based on transfer learning, scene information is also recognized efficiently. Finally, the semantic descriptors are modeled by two cascaded And-Or Graphs (AoGs) constraining the spatial scenarios and temporal sequences. Our unified approach is demonstrated on two public benchmarks containing long-term activities and on an escort robot for real-world applications. • A segmentation–detection–recognition hierarchical framework is proposed to infer behavior intentions for natural human–robot visual interaction. • We design an efficient body part detection scheme by segmenting 3D behavior cubes and fusing local features. • We present a new three-stream context CNN capturing global spatio-temporal relationships of sub-behaviors. • An inference model with two cascaded AoGs constraining spatial scenarios and temporal sequences is constructed. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel method for dynamic label assignment in temporal action detection (TAD) called Optimal Action Segment Assignment (OASA). The OASA method converts label assignment into an optimal transportation problem by computing the cost matrix between predicted temporal action segments and groundtruths. The unit transportation cost between a predicted temporal segment and a groundtruth pair is defined as the weighted summation of action classification loss and temporal localization loss. Additionally, we deploy Adaptive Estimation of Candidate Segment Number (AE-CSN) to adaptively determine the number of positive samples for each groundtruth. After formulation, the label assignment problem is converted to find a global optimal assignment plan by minimizing the cost. Therefore, OASA eliminates the need for manually designed prior parameters, which exist in fixed label assignment methods, and improves the generalization of the algorithm between different datasets. To evaluate OASA, we also introduce a simple anchor-free temporal action detector called ActionMixer. It consists of two components: Temporal Mixer and Channel Mixer. The Temporal Mixer employs depth-wise convolution layers with large kernels to capture temporal information, while the Channel Mixer mixes and extracts features across the channel dimension. Extensive experiments conducted on the THUMOS-14, ActivityNet-1.3, and EPIC-Kitchens-100 datasets show that ActionMixer equipped with OASA achieves state-of-the-art performance, surpassing other advanced temporal action detection methods. • Optimal Action Segment Assignment is proposed for Temporal Action Detection. • Optimal Action Segment Assignment removes the temporal prior. • ActionMixer is proposed to evaluate Optimal Action Segment Assignment. • ActionMixer achieves state-of-the-art performance on the popular benchmarks. [ABSTRACT FROM AUTHOR]

Local feature matching constitutes the cornerstone of multiple computer vision applications (e.g., 3D reconstruction and long-term visual localization), and has been successfully resolved by detector-free methods. To further improve the matching performance, more recent research has focused on designing sophisticated architectures but endures additional computational overhead. In this study, with a different perspective from previous studies, we aim to develop a deep and compact matching network to improve performance while reducing computing cost. The key insight is that a local feature matcher with deep layers can capture more human-intuitive and simpler-to-match features. To this end, we propose DeepMatcher, a deep transformer-based network that tackles the inherent obstacles of not being able to build a deep local feature matcher with current methods. DeepMatcher consists of: (1) a local feature extractor (LFE), (2) a feature-transition module (FTM), (3) a slimming transformer (SlimFormer), (4) a coarse matches module (CMM), and (5) a fine matches module (FMM). The LFE is utilized to generate dense keypoints with enriched features from the images. We then introduce the FTM to ensure a smooth transition of feature scopes from LFE to the subsequent SlimFormer because of their different receptive fields. Subsequently, we develop SlimFormer dedicated to DeepMatcher, which leverages vector-based attention to model the relevance among all keypoints, enabling the network to construct a deep Transformer architecture with less computational cost. Relative position encoding is applied to each SlimFormer to explicitly disclose the relative distance information, thereby improving the representation of the keypoints. A layer-scale strategy is also employed in each SlimFormer to enable the network to adaptively assimilate message exchange, thus endowing it to simulate human behavior, in which humans can acquire different matching cues each time they scan an image pair. By interleaving the self- and cross-SlimFormers multiple times, DeepMatcher can easily establish pixel-wise dense matches at the coarse level using the CMM. Finally, we consider match refinement as a combination of classification and regression problems and design an FMM to predict confidence and offset concurrently, thus generating robust and accurate matches. Compared with our baseline LoFTR in indoor/outdoor pose estimation, DeepMatcher surpasses it by 3.32%/2.91% in AUC@5 ∘. Besides, DeepMatcher and DeepMatcher-L significantly reduce computational cost and only consume 77.89% and 92.46% GFLOPs of LoFTR. Large DeepMatcher considerably outperforms state-of-the-art methods on several benchmarks, including outdoor pose estimation (MegaDepth dataset), indoor pose estimation (ScanNet dataset), homography estimation (HPatches dataset), and image matching (HPatches dataset), demonstrating the superior matching capability of a deep local feature matcher. • We propose DeepMatcher, a deep Transformer-based network for local feature matching. • We propose SlimFormer to enable DeepMatcher to be extended into dozen layers. • We propose FTM to ensure a smooth transition from CNN to SlimFormer. • We propose FMM to optimize coarse matches, deriving robust and accurate matches. [ABSTRACT FROM AUTHOR]

Local feature matching involves the task of establishing the pixel-wise correspondences between a pair of images. As an integral component of plentiful computer vision applications (e.g., visual localization), this task has been successfully performed using Transformer-based methods. However, these methods typically extract numerous keypoints from sparse texture regions to construct a densely-connected graph neural network (GNN) for long-range feature aggregation, which inevitably triggers redundant message exchange and hampers the learning process. Furthermore, they employ transformer encoder layers that consider images as 1D sequences, leaving them incapable of extracting multiscale local structural information from the images, which is critical for establishing correspondence in image pairs with significantly scales shifts. In this study, we develop FMAP, an innovative detector-free approach that enables accurate local feature matching. For the first issue, FMAP employs an anchor points feature aggregation module (APAM) that captures representative keypoints and discards the extraneous keypoints to build a sparsified GNN for compact yet clean message exchange, with the key insight that the keypoints containing abundant visual information are distinguishable from their neighbors. For the second issue, FMAP proposes a global–local multiscale perception module (GMPM), which incorporates abundant multiscale local context information into global feature representation by employing multiple depth-wise convolutions with varying kernel sizes, thereby generating discriminative features that are robust to scale shifts. In addition, the depth-wise convolutions are utilized in the feed-forward network of the GMPM to further fuse the global context information and local feature representation. Extensive experiments on several standard benchmarks demonstrate that the proposed FMAP method significantly outperforms state-of-the-art methods. Compared to the cutting-edge methods MatchFormer, QuadTree, and TopicFM in relative pose estimation task, FMAP surpasses them by 2.27%, 0.58%, and 1.08% in terms of AUC@5°. Besides, FMAP noticeably outperforms the baseline LoFTR by (2. 38 % , 1. 89 % , 1. 45 %) in terms of AUC@(5°, 10°, 20°). Moreover, we integrate FMAP into an official visual localization framework and conduct a visual localization experiment, with the results showing that FMAP exceeds LoFTR by 2.3% in terms of AP. • We propose a Transformer-based method that achieves excellent matching accuracy. • We constructs sparsified GNN to alleviate redundant message exchange. • We integrate global context and multi-scale local features to boost robustness. [ABSTRACT FROM AUTHOR]