Furthermore, we swap the dimensions of \(\widehat{{T_{2}^{*} }}\) and then adopt the sigmoid activation function to achieve the interaction attention weight, which is multiplied by \(T\) to obtain the re-weighted RoI feature \(B_{2}\). The above process is denoted in Eq. (12...
whose joint action of both the energy-momentum tensors is denoted byT^{\text {eff}}_{\epsilon \nu }~\big (=T_{\epsilon \nu }+\theta ^{*}_{\epsilon \nu }\big ), where\theta ^{*}_{\nu \epsilon }=\alpha \theta _{\nu \epsilon }. In terms of the affine connection, the ...
Item 4 is due partly to the effects of vehicle shadows, which tend to connect vehicles, especially when seen in the distance. The morphological operations that were applied (see below) also tended to make vehicles become joined. Item 5 is never manifest in the road region, i.e. between ...
High sparseness and irregularity by nature and the absence of texture attributes are the primary characteristics of a point cloud, which is well distinguished from image array. Since we have already known how fast light travels, the distance of obstacles could be determined without effort. LiDAR ...
Thus, the feathered alpha values are given by: (6.2)alpha=distance/feather_distance+1∗opaque_value where distance is the distance of the pixel from the shape boundary, whereas feature_distance specifies the number of pixels to feather, and opaque_value is the value in the alpha mask. 6.6...
The structure of DSCBlock, illustrated in Fig.4. Initially, the feature mapFofW×W×Mdimension and is separated into feature channels using depth convolutionK, resulting in a combined set of segregated feature channels, denoted asS. Subsequently, a deformable weighted convolutional operator and chann...
For training, the cosine distance δ(·, ·) to a sketch 15085 anchor (s) from a negative photo (p−), denoted as β− = δ(fs, fp− ) should increase while that from the positive photo (p+), β+ = δ(fs, fp+ ) should decrease. Training is done via triplet l...
The average overlap with the VOT2015 annotations is denoted by Avg. overlap, while the #opt. failures denotes the number of frames in which the algorithm switched from constrained to unconstrained optimization. %frames #frames fg-out bg-in Avg. overlap #opt. failures Automatic GT 88 % 18875 ...
the defects on the leather are visually examined by expert personnel and the quality of the leather is classified. A dataset was created with photographs taken while expert personnel were working to detect defects. A distance of 0.5 m was generally preferred when taking photographs. However, shots...
Rui Qian, Xin Lai, Xirong Li: 3D Object Detection for Autonomous Driving: A Survey (Pattern Recognition 2022: IF=8.518) - rui-qian/SoTA-3D-Object-Detection