  Data imbalance and open-ended distribution are two intrinsic characteristics
of the real visual world. Though encouraging progress has been made in tackling
each challenge separately, few works dedicated to combining them towards
real-world scenarios. While several previous works have focused on classifying
close-set samples and detecting open-set samples during testing, it's still
essential to be able to classify unknown subjects as human beings. In this
paper, we formally define a more realistic task as distribution-agnostic
generalized category discovery (DA-GCD): generating fine-grained predictions
for both close- and open-set classes in a long-tailed open-world setting. To
tackle the challenging problem, we propose a Self-Balanced Co-Advice
contrastive framework (BaCon), which consists of a contrastive-learning branch
and a pseudo-labeling branch, working collaboratively to provide interactive
supervision to resolve the DA-GCD task. In particular, the contrastive-learning
branch provides reliable distribution estimation to regularize the predictions
of the pseudo-labeling branch, which in turn guides contrastive learning
through self-balanced knowledge transfer and a proposed novel contrastive loss.
We compare BaCon with state-of-the-art methods from two closely related fields:
imbalanced semi-supervised learning and generalized category discovery. The
effectiveness of BaCon is demonstrated with superior performance over all
baselines and comprehensive analysis across various datasets. Our code is
publicly available.
