This paper studies the few-shot segmentation (FSS) task, which aims to segment objects belonging to unseen categories in a query image by learning a model on a small number of well-annotated support samples. Our analysis of two mainstream FSS paradigms reveals that the predictions made by prototype learning methods are usually conservative, while those of affinity learning methods tend to be more aggressive. This observation motivates us to balance the conservative and aggressive information captured by these two types of FSS frameworks so as to improve the segmentation performance. To achieve this, we propose a **P**rototype-**A**ffinity **H**ybrid **Net**work (PAHNet), which introduces a Prototype-guided Feature Enhancement (PFE) module and an Attention Score Calibration (ASC) module in each attention block of an affinity learning model (called affinity learner). These two modules utilize the predictions generated by a pre-trained prototype learning model (called prototype predictor) to enhance the foreground information in support and query image representations and suppress the mismatched foreground-background (FG-BG) relationships between them, respectively. In this way, the aggressiveness of the affinity learner can be effectively mitigated, thereby eventually increasing the segmentation accuracy of our PAHNet method. Experimental results show that PAHNet outperforms most recently proposed methods across 1-shot and 5-shot settings on both PASCAL-5$^i$ and COCO-20$^i$ datasets, suggesting its effectiveness. The code is available at: [GitHub - tianyu-zou/PAHNet: Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation (ICCV'25)](https://github.com/tianyu-zou/PAHNet)

This paper studies the challenging task of Referring Expression Comprehension (REC), which aims at detecting the text-referred target object in an input image. To achieve this, most recent works attempt to adapt powerful pretrained models through integrating additional structures (e.g., low-rank adaptation (LoRA) or adapter modules) to enable efficient parameter tuning. However, all these methods process pretrained features in a position-agnostic manner. This will limit their effectiveness in REC tasks, where the positional information is essential to correctly localize the target object. To this end, we propose a novel parameter-efficient tuning approach, named Multi-Modal Adaptive Positional Encoding (MAP), which addresses the above problem from a new perspective of positional encoding. More specifically, MAP first generates initial positional embeddings for different visual encoder layers from a set of learnable vectors, and then adjusts them adaptively based on spatial-wise visual-linguistic correlations of input data. In this way, the positional information of different image tokens can be appropriately modeled and utilized by MAP, thus making it more applicable to REC tasks. Extensive experiments on five widely-used datasets demonstrate that MAP achieves comparable results to full fine-tuning methods with much fewer extra parameters and outperforms other parameter-efficient tuning approaches. Our source code is available at: https://github.com/Mr-Bigworth/MAP.