In practical application scenarios of generalized category discovery (GCD), the data is often fine-grained, e.g., assisting biologists in classifying newly captured insect photographs. However, most existing GCD methods don’t focus on the subtle variance among fine-grained data and design models based on this characteristic. Motivated by this, we propose a new negative relation steering (namely NegReS) method for fine-grained GCD, which can better capture the subtle variance by exploring implicit relations among fine-grained data. Considering that the generic positive information is not available or reliable, we turn to excavate negative but effective relations, i.e., semantic-wise and instance-wise negative relations, and take full advantage of them to enhance the discriminative ability of the parametric classification in the GCD model. Specifically, semantic-wise negative relations among spatiality-related attention maps are employed to help capture more discriminative regions. Meanwhile, instance-wise negative relations among unlabeled data are excavated to guide the classifier optimization. Extensive experiments on several benchmarks demonstrate the effectiveness of our method.