Abstract: Embodiments of the present disclosure relate to fine-tuning a neural network model using a weight-decomposed low-rank adaptation (DoRA). DoRA reduces the number of parameters that are fine-tuned, thereby reducing memory and the time needed to fine-tune the parameters. the Pre-trained weights are decomposed into two components, magnitude and direction, which are separately fine-tuned. The magnitude components are fine-tuned while the direction components remain unchanged (frozen). Then low-rank adaptation (LoRA) is used to fine-tune the direction components, efficiently minimizing the number of trainable parameters. Compared with using LoRA to fine-tune the weights directly, using DoRA exhibits a closer resemblance to full fine-tuning's learning behavior and improves upon LoRA in commonsense reasoning and visual instruction tuning tasks. By employing DoRA, both the learning capacity and training stability of LoRA is enhanced.

Abstract: Semantic segmentation generally refers to a machine learning process that associates a label or category with every pixel in an image. This can be used to recognize a collection of pixels that form distinct categories of objects, which may have applications in autonomous driving for example where the vehicle needs to identify other vehicles, pedestrians, traffic signs, pavement, and other road features from captured images of a surrounding environment. While using pixel-level annotations may be ideal for fully-supervised training of the semantic segmentation model, collecting such annotations is time-consuming and expensive, and therefore limits the scalability and practicality of fully-supervised training methods. The present disclosure enables weakly supervised training of a semantic segmentation model aided by learned prompts embedded with semantic knowledge.

Abstract: A domain adaptation module is used to optimize a first domain derived from a second domain using respective outputs from respective parallel hidden layers of the domains.

Abstract: A mold assembly has a pressing mold, a shunt cone, and a mold cap. The shunt cone is disposed in the pressing mold to define a conical pressing channel. The shunt cone has a body, an inlet channel, and an outlet channel. The body has a conical cone portion. The inlet channel has an extension height from a top of shunt cone being higher than an extension height of the inlet channel from the top of the shunt cone. The mold cap is attached to a bottom of the pressing mold and has a lower mold cavity to allow the cone portion to extend into the lower mold cavity and to define a conical shaped connection channel. The connection channel communicates with the pressing channel, and a material input channel is defined in the mold cap and communicating with the connection channel.

Abstract: A domain adaptation module is used to optimize a first domain derived from a second domain using respective outputs from respective parallel hidden layers of the domains.

Abstract: A domain adaptation module is used to optimize a first domain derived from a second domain using respective outputs from respective parallel hidden layers of the domains.

Abstract: According to a network space search method, an expanded search space is partitioned into multiple network spaces. Each network space includes a plurality of network architectures and is characterized by a first range of network depths and a second range of network widths. The performance of the network spaces is evaluated by sampling respective network architectures with respect to a multi-objective loss function. The evaluated performance is indicated as a probability associated with each network space. The method then identifies a subset of the network spaces that has the highest probabilities, and selects a target network space from the subset based on model complexity.

Abstract: A domain adaptation module is used to optimize a first domain derived from a second domain using respective outputs from respective parallel hidden layers of the domains.

Abstract: Embodiments herein treat the action segmentation as a domain adaption (DA) problem and reduce the domain discrepancy by performing unsupervised DA with auxiliary unlabeled videos. In one or more embodiments, to reduce domain discrepancy for both the spatial and temporal directions, embodiments of a Mixed Temporal Domain Adaptation (MTDA) approach are presented to jointly align frame-level and video-level embedded feature spaces across domains, and, in one or more embodiments, further integrate with a domain attention mechanism to focus on aligning the frame-level features with higher domain discrepancy, leading to more effective domain adaptation. Comprehensive experiment results validate that embodiments outperform previous state-of-the-art methods. Embodiments can adapt models effectively by using auxiliary unlabeled videos, leading to further applications of large-scale problems, such as video surveillance and human activity analysis.

Abstract: Embodiments herein treat the action segmentation as a domain adaption (DA) problem and reduce the domain discrepancy by performing unsupervised DA with auxiliary unlabeled videos. In one or more embodiments, to reduce domain discrepancy for both the spatial and temporal directions, embodiments of a Mixed Temporal Domain Adaptation (MTDA) approach are presented to jointly align frame-level and video-level embedded feature spaces across domains, and, in one or more embodiments, further integrate with a domain attention mechanism to focus on aligning the frame-level features with higher domain discrepancy, leading to more effective domain adaptation. Comprehensive experiment results validate that embodiments outperform previous state-of-the-art methods. Embodiments can adapt models effectively by using auxiliary unlabeled videos, leading to further applications of large-scale problems, such as video surveillance and human activity analysis.

Abstract: A domain adaptation module is used to optimize a first domain derived from a second domain using respective outputs from respective parallel hidden layers of the domains.

Abstract: A domain adaptation module is used to optimize a first domain derived from a second domain using respective outputs from respective parallel hidden layers of the domains.

Abstract: A domain adaptation module is used to optimize a first domain derived from a second domain using respective outputs from respective parallel hidden layers of the domains.

Abstract: A computing device, programmed to: acquire a color image and transform the color image into a color-component map. The computer can be further programmed to process the color-component map to detect a traffic sign by determining spatial coincidence and determining temporal consistency between the color-component map and the traffic sign.

Abstract: A computing device, programmed to: acquire a color image and transform the color image into a color-component map. The computer can be further programmed to process the color-component map to detect a traffic sign by determining spatial coincidence and determining temporal consistency between the color-component map and the traffic sign.

Abstract: A buffer member includes a first buffer portion, a pin, a bending portion and a second buffer portion. The pin is located on the first buffer portion. The bending portion is connected with the first buffer portion. The second buffer portion is connected with the bending portion, and the first buffer portion is connected with the second buffer portion via the bending portion. A reinforcing member is located inside the pin for raising the rigidity of the pin. The pin is used for fixing a vibration source. The first buffer portion and the second buffer portion reduce the transmission of the vibration energy from the vibration source.

Abstract: A buffer member includes a first buffer portion, a pin, a bending portion and a second buffer portion. The pin is located on the first buffer portion. The bending portion is connected with the first buffer portion. The second buffer portion is connected with the bending portion, and the first buffer portion is connected with the second buffer portion via the bending portion. A reinforcing member is located inside the pin for raising the rigidity of the pin. The pin is used for fixing a vibration source. The first buffer portion and the second buffer portion reduce the transmission of the vibration energy from the vibration source.

Abstract: A buffer member includes a first buffer portion, a pin, a bending portion and a second buffer portion. The pin is located on the first buffer portion. The bending portion is connected with the first buffer portion. The second buffer portion is connected with the bending portion, and the first buffer portion is connected with the second buffer portion via the bending portion. A reinforcing member is located inside the pin for raising the rigidity of the pin. The pin is used for fixing a vibration source. The first buffer portion and the second buffer portion reduce the transmission of the vibration energy from the vibration source.

Abstract: A buffer member includes a first buffer portion, a pin, a bending portion and a second buffer portion. The pin is located on the first buffer portion. The bending portion is connected with the first buffer portion. The second buffer portion is connected with the bending portion, and the first buffer portion is connected with the second buffer portion via the bending portion. A reinforcing member is located inside the pin for raising the rigidity of the pin. The pin is used for fixing a vibration source. The first buffer portion and the second buffer portion reduce the transmission of the vibration energy from the vibration source.

Abstract: An overlay mark applicable in a non-volatile memory includes two first X-direction isolation structures, two first Y-direction isolation structures, two second X-direction isolation structures, two second Y-direction isolation structures, a first dielectric layer, and a conductive layer. The first X-direction isolation structures, the first Y-direction isolation structures, the second X-direction isolation structures, and the second Y-direction isolation structures are disposed in a substrate. The first X-direction isolation structures and the first Y-direction isolation structures are arranged to a first rectangle, the second X-direction isolation structures and the second Y-direction isolation structures are arranged to a second rectangle, and the second rectangle is located in the first rectangle. The first dielectric layer is disposed on a surface of the substrate. The conductive layer is disposed on the first dielectric layer.