Edge labels are typically at various granularity levels owing to the varying preferences of annotators, thus handling the subjectivity of per-pixel labels has been a focal point for edge detection. Previous methods often employ a simple voting strategy to diminish such label uncertainty or impose a strong assumption of labels with a pre-defined distribution, e.g., Gaussian. In this work, we unveil that the segment anything model (SAM) provides strong prior knowledge to model the uncertainty in edge labels. Our key insight is that the intermediate SAM features inherently correspond to object edges at various granularities, which reflects different edge options due to uncertainty. Therefore, we attempt to align uncertainty with granularity by regressing intermediate SAM features from different layers to object edges at multi-granularity levels. In doing so, the model can fully and explicitly explore diverse ``uncertainties'' in a data-driven fashion. Specifically, we inject a lightweight module (~ 1.5% additional parameters) into the frozen SAM to progressively fuse and adapt its intermediate features to estimate edges from coarse to fine. It is crucial to normalize the granularity level of human edge labels to match their innate uncertainty. For this, we simply perform linear blending to the real edge labels at hand to create pseudo labels with varying granularities. Consequently, our uncertainty-aligned edge detector can flexibly produce edges at any desired granularity (including an optimal one). Thanks to SAM, our model uniquely demonstrates strong generalizability for cross-dataset edge detection. Extensive experimental results on BSDS500, Muticue and NYUDv2 validate our model's superiority.

Text-to-motion generation has attracted increasing attention in recent years. Existing methods primarily employ Vector Quantized Variational AutoEncoder (VQ-VAE) as the tokenizer for motion representation. However, such vector quantization maps the continuous space into limited discrete tokens, which inevitably leads to significant information loss. To address this limitation, we propose a simple yet effective approach to expand the capacity of discrete motion representation space, effectively reducing the information loss without incurring additional overhead. Specifically, we present a Multi-Head Generative Masked model which a) exploits multi-head mechanism into quantization for a high-fidelity motion tokenizer, and b) simultaneously generates tokens across different heads using a bi-directional transformer. In this way, the motion representation space can be expanded exponentially at the cost of negligible time and parameter overhead. Extensive experiments demonstrate that our method achieves state-of-the-art performance on both HumanML3D and KIT-ML datasets.