Stance detection aims to identify the viewpoint (favor, against, or neutral) expressed in text towards a specific target. Recent studies on zero-shot and few-shot stance detection focus primarily on learning generalized representations from explicit targets. However, these methods often neglect implicit yet semantically important targets and fail to sufficiently exploit fine-grained contextual cues, limiting model performance in nuanced scenarios. To overcome these limitations, we propose a novel two-stage framework: First, a data augmentation framework named Hierarchical Collaborative Target Augmentation (HCTA) employs Large Language Models (LLMs) to identify and annotate implicit targets via Chain-of-Thought (CoT) prompting and multi-model voting, significantly enriching training data with latent semantic relations. Second, we introduce FiCAN, a Fine-grained Context-aware Attention Network, integrating a joint text-target encoding and a sparse cross-attention mechanism to selectively capture critical fine-grained contextual clues. Experiments on the VAST benchmark dataset demonstrate that our approach achieves state-of-the-art results, confirming the effectiveness of implicit target augmentation and fine-grained contextual modeling.