This paper proposes the use of an event camera as a component of a vision system that enables counting of fast-moving objects – in this case, falling corn grains. These type of cameras transmit information about the ...
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Reinforcement learning is of increasing importance in the field of robot control and simulation plays a key role in this process. In the unmanned aerial vehicles (UAVs, drones), there is also an increase in the number...
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3D object detection from LiDAR sensor data is an important topic in the context of autonomous cars and drones. In this paper, we present the results of experiments on the impact of backbone selection of a deep convolu...
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Transformers have achieved remarkable success in several domains, ranging from natural language processing to computervision. Nevertheless, it has been recently shown that stacking self-attention layers — the distin...
ISBN:
(纸本)9781713871088
Transformers have achieved remarkable success in several domains, ranging from natural language processing to computervision. Nevertheless, it has been recently shown that stacking self-attention layers — the distinctive architectural component of Transformers — can result in rank collapse of the tokens' representations at initialization. The question of if and how rank collapse affects training is still largely unanswered, and its investigation is necessary for a more comprehensive understanding of this architecture. In this work, we shed new light on the causes and the effects of this phenomenon. First, we show that rank collapse of the tokens' representations hinders training by causing the gradients of the queries and keys to vanish at initialization. Furthermore, we provide a thorough description of the origin of rank collapse and discuss how to prevent it via an appropriate depth-dependent scaling of the residual branches. Finally, our analysis unveils that specific architectural hyperparameters affect the gradients of queries and values differently, leading to disproportionate gradient norms. This suggests an explanation for the widespread use of adaptive methods for Transformers' optimization.
Reinforcement learning is of increasing importance in the field of robot control and simulation plays a key role in this process. In the unmanned aerial vehicles (UAVs, drones), there is also an increase in the number...
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This paper proposes the use of an event camera as a component of a vision system that enables counting of fast-moving objects – in this case, falling corn grains. These type of cameras transmit information about the ...
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3D object detection from LiDAR sensor data is an important topic in the context of autonomous cars and drones. In this paper, we present the results of experiments on the impact of backbone selection of a deep convolu...
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Clustering of motion trajectories is highly relevant for human-robot interactions as it allows the anticipation of human motions, fast reaction to those, as well as the recognition of explicit gestures. Further, it al...
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Presents a method whereby an autonomous mobile robot automatically selects the most informative data from a set of images acquired a priori, using a statistical method termed information sampling. These data could be ...
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Presents a method whereby an autonomous mobile robot automatically selects the most informative data from a set of images acquired a priori, using a statistical method termed information sampling. These data could be a single pixel or a number scattered throughout an image. This information is then used to build a topological map of the environment. Our sole input data are omnidirectional images obtained from a catadioptric panoramic camera. Experimental results show that by using only the best data the topological position of a robot, visually maneuvering through a simple indoor environment, can easily be determined.
In skeleton-based action recognition, Graph Convolutional Networks model human skeletal joints as vertices and connect them through an adjacency matrix, which can be seen as a local attention mask. However, in most ex...
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