Class incremental semantic segmentation (CISS) enables a model to continually segment new classes from non-stationary data while preserving previously learned knowledge. Recent top-performing approaches are prototype-based methods that assign a prototype to each learned class to reproduce previous knowledge. However, modeling each class distribution relying on only a single prototype, which remains fixed throughout the incremental process, presents two key limitations: (i) a single prototype is insufficient to accurately represent the complete class distribution when incoming data stream for a class is naturally multimodal; (ii) the features of old classes may exhibit anisotropy during the incremental process, preventing fixed prototypes from faithfully reproducing the matched distribution. To address the aforementioned limitations, we propose a Continual Gaussian Mixture Distribution (CoGaMiD) modeling method. Specifically, the means and covariance matrices of the Gaussian Mixture Models (GMMs) are estimated to model the complete feature distributions of learned classes.

We set the patch size to be 8. Our model is optimized by AdamW optimizer [3] with a learning rate2 of 0.0004, 250k training steps, linearly warm-up of 5000 steps and an exponentially weight-decaying3 schedule. The gradient norm is clipped at 1. We use Pytorch automatic mixed-precision and data4 paralleling for training acceleration. All models are trained on 4 Nvidia RTXA5000 GPUs with a5 total batch size of 128.

Learning object-centric representations from complex natural environments enables both humans and machines with reasoning abilities from low-level perceptual features. To capture compositional entities of the scene, we proposed cyclic walks between perceptual features extracted from vision transformers and object entities. First, a slot-attention module interfaces with these perceptual features and produces a finite set of slot representations. These slots can bind to any object entities in the scene via inter-slot competitions for attention. Next, we establish entity-feature correspondence with cyclic walks along high transition probability based on the pairwise similarity between perceptual features (aka "parts") and slot-binded object representations (aka "whole").

"freely" into massive pre-training datasets to support large-scale pre-training.

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

In this section, we provide further elaboration on this fine-tuning method.

The whole is greater than its parts and the parts constitute the whole.