Contrastive Clustering loss and update the prototypical vectors.Algorithm 1: Prototypical Constrative Clustering Loss Compute Prototypical Constrative Clustering Loss based on Eqn. Compute Prototypical Constrative Clustering Loss based on Eqn. Two segmentation network architectures have been used in our experiments, i.e., (1) DeepLab-V3 The learning rate is set individually for each step and dataset. Similarly, to illustrate the effectiveness and robustness of our method in the non-incremental setting. We also perform an additional ablation study on the ADE20K (100-50) benchmark to investigate the impact of the delta.

We provide the detailed of the PQ retrieval performance trends of Glint360K trained using the CosFace loss in Tab. 1.

While the object detection task requires a huge number of annotated samples to guarantee its performance, placing bounding boxes for every object in each sample is time-consuming and costs alot.

Toinvestigatethisproblem, wemine theProtein Data Bank (PDB) [1]toconstruct alargedataset of protein complex structures for which structures of the individual proteins on their own are not available.