Neural Information Processing Systems http://nips.cc/

In this way, even with limited meta samples, MTG-Net holds the potential to produce reasonable gain estimations on arbitrary task combinations.

We introduce a novel, training-free method for sampling differentiable representations (diffreps) using pretrained diffusion models. Rather than merely mode-seeking, our method achieves sampling by "pulling back" the dynamics of the

Neural Information Processing Systems http://nips.cc/

The base model takes surround-view images of the ego-vehicle as input. As shown in Figure 1, we provide further visual comparisons of HD map vectorization results. The results reaffirm the necessity of a rasterization perspective in map vectorization. Figure 1 presents more visualization of MapVR's HD map construction results. As discussed in Section 3, the Chamfer-distance-based metric struggles to offer a fair evaluation for such scenarios.

MapVR (Map V ectorization via Rasterization), a novel framework that applies differentiable rasterization to vectorized outputs and then performs precise and geometry-aware supervision on rasterized HD maps.

Experimental data does not suffer from confounding but is usually limited in both scope and scale.

Almost Linearity T argeting, based on medium-scale almost linearity assumptions.