Then we introduce a semantic mapper that produces the proxy masks from various input conditions including semantic masks.

Participants are not allowed to modify the training procedure.

We refer to the proposed solution as SA3D, for Segment Anything in 3D. It is only required to provide a manual segmentation prompt ( e.g., rough points) for the target object in a single view, which is used to generate its 2D mask in this view with SAM.

The "black-box" nature of representations, combined with the known susceptibility of networks to learn spurious correlations [

Neural circuits are typically comprised of populations of primary (excitatory) neurons and local (inhibitory) interneurons.

Modern image generation (IG) models have been shown to capture rich semantics valuable for image understanding (IU) tasks. However, the potential of IU models to improve IG performance remains uncharted. We address this issue using a token-based IG framework, which relies on effective tokenizers to map images into token sequences. Currently, pixel reconstruction (e.g., VQGAN) dominates the training objective for tokenizers. In contrast, our approach adopts the feature reconstruction objective, where tokenizers are trained by distilling knowledge from pretrained IU encoders. Comprehensive comparisons indicate that tokeniz-ers with strong IU capabilities achieve superior IG performance across a variety of metrics, datasets, tasks, and proposal networks.

This protocol is often regarded as a "golden compass" for navigating SoT A detectors. But can these stand-out "winners" be