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Ablation experiments on the value of parameters a and b. 4.5.2. The Architecture of MLDAC As shown in Table 5, the combination of different schemes was validated to search for the optimal settings of MLDAC. As shown in Table 5, our proposed learnable linear positional encoding and skip connection are, indeed, effective. The former enhances the connections between different features, and the latter strengthens the semantic information, making it easier to obtain the correlated region between the supporting and query images. Meanwhile, the 1/4 and 1/8 features accomplish the segmentation task in a more effective way.
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Nowadays, autonomous driving technology has become widely prevalent. The intelligent vehicles have been equipped with various sensors (e.g. vision sensors, LiDAR, depth cameras etc.). Among them, the vision systems with tailored semantic segmentation and perception algorithms play critical roles in scene understanding. However, the traditional supe...
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... it is offset to 0 or 1, the network degenerates into an ordinary single-target structure. The experimental results obtained using different values of a are shown in Figure 5(1). It can be seen that when a = 0.15, the model achieves the optimal result; therefore, we set a = 0.15 for all other experiments. ...
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