# SemABC: Semantic-Guided Adaptive Bias Calibration for Generative Zero-Shot Point Cloud Segmentation
## 1 Introduction  
Among three-dimensional (3D) scene understanding tasks, point cloud segmentation plays a fundamental role in fields such as autonomous driving [1, 2], robot navigation [3, 4], and augmented reality [5]. However, due to the sparsity and unordered properties of 3D point clouds, as well as the high cost of acquiring high-quality annotations [6], the supervised learning methods for point clouds [7, 8] face challenges in generalization to a wider range of scenarios. Since generalized zero-shot learning (GZSL) [9, 10, 11] enables knowledge transfer from seen to unseen classes without the need for labeled data during training, recently, researchers have been particularly interested in exploring the potential of these techniques to handle the prediction of both seen and unseen classes in point cloud segmentation tasks. GZSL can be divided into transductive approaches [12, 13, 14], which leverage data samples from unseen classes during training, and inductive approaches [15, 16, 17], which rely solely on the samples of seen classes during training. Given the difficulties of acquiring all kinds of unlabeled data samples in real-world scenarios, inductive GZSL is more suitable for varied practical applications. Inductive GZSL methods for point cloud semantic segmentation, such as 3 DGenZ [18] and SV-Seg [19], employ generative models [20,21] to create synthesized features based on semantic embeddings [22, 23, 24, 25] of unseen classes. However, since the generator is trained exclusively on seen classes, the features it generates for unseen classes are inevitably biased toward the characteristics of seen classes. Consequently, the segmentor trained with these features tends to favor the seen classes. To address this issue, these approaches [18,19] typically reduce the bias toward seen classes by subtracting a predefined constant value from their predicted probability score for calibration purposes, which may affect the generalization and robustness of the model in open scenarios. To address this problem, we propose a semantic-guided adaptive bias calibration approach with a dual-branch network architecture. This network comprises a primary segmentation branch (main branch) and a visual-semantic fusion branch (auxiliary branch). The primary segmentation branch encodes the input point cloud and predicts the segmentation probabilities, as other approaches do, while the visual-semantic fusion branch additionally explores the correlation between the visual features of the point clouds and the semantic embeddings of the categories to reduce the bias toward seen classes. Our motivation is that, since these synthesized features are generated based on category semantics of unseen classes, their relevance to semantics is inherently higher than that of the seen classes. Therefore, we propose to incorporate the spatial and channel attention mechanisms to exploit the visual-semantic correlation and align the two modalities via contrastive learning, as shown in the first row of Figure 1. In this way, the visual-semantic fusion branch is able to produce segmentation predictions in favor of unseen classes, which forms a complement to those from the primary segmentation branch. With this dual-branch network architecture, we further propose an adaptive bias calibration module that dynamically adjusts the weights of the auxiliary predictions based on the prediction of the main branch, as shown in the bottom row of Figure 1. When the main branch exhibits high confidence with the predicted probability, we assign less weight to the auxiliary predictions to avoid introducing additional noise or making unnecessary corrections to high-confidence predictions. In contrast, when confidence is low, we increase the influence of the auxiliary predictions, leveraging different perspectives from the auxiliary branch to enhance the reliability of the overall prediction.