Anchors as Boundaries：Integrating Bilateral Contrastive Learning and BMIU for Deep Imbalanced Regression

Imbalanced regression tackles learning tasks where the labels are continuous but the sample distribution across equidistant label-bins is skewed, with its deep learning extension forming the emerging field of Deep Imbalanced Regression (DIR). Existing algorithms implicitly assume a consistent similarity pattern among samples within equidistant label-bins. For example, this presumption implies uniform facial aging rates across age groups and identical depth perception cues across spatial intervals. However, our preliminary experiments have empirically refuted these hypotheses. Furthermore, DIR also confronts challenges such as significant intra-bin sample heterogeneity and overlapping decision boundaries between adjacent bins. To address the aforementioned challenges, we propose BCLU , a novel strategy integrating Bilateral Contrastive Learning (BCL) and Bilateral Mixup-Induced Universum (BMIU) augmentation, along with the corresponding RBLU algorithm. On one hand, BCL dynamically partitions data into left and right subsets anchored at adaptively determined boundaries, enabling unidirectional matching of strongly similar positive pairs within each subset. This mechanism overcomes the dilemma of constructing negative pairs with weak label similarity but strong semantic affinity, thereby relaxing the homogeneous assumption. Experimental results demonstrate BCL's effectiveness in mitigating the imbalance of samples. On the other hand, BMIU generates hybrid features by mixing anchors with randomly selected samples from bilateral subsets, constructing wedge-shaped features that belong to no specific label-bin. This strategy enforces the discriminant boundary between label-bin to be maximally separated while ensuring that the features within each label-bins are as compact as possible, thereby suppressing inter-bin overlaps. Notably, BMIU inherently preserves label-ordering consistency without explicit any ordinal constraints. Extensive experiments on standard DIR benchmarks thoroughly validate the superior efficacy of the proposed RBLU algorithm.