Recently, machine learning-based techniques have been applied for layout hotspot detection. However, existing methods encounter challenges in capturing the decision boundary across the entire dataset and ignore the geometric properties and topology of the polygons. In this paper, we introduce CLI-HD, a novel contrastive learning framework on layout sequences and images for hotspot detection. Our framework improves the ability to distinguish between hotspots and non-hotspots by similarity computations instead of a single decision boundary. To effectively incorporate geometric information into the model training process, we propose Layout2Seq, which encodes polygon shapes as vectors within sequences that are subsequently fed into the CLIHD. Furthermore, to better represent topology information, we develop an absolute position embedding, replacing the standard position encoders used in Transformer architectures. Extensive evaluations on various benchmarks demonstrate that CLI-HD outperforms current state-of-the-art methods, with an accuracy improvement ranging from $0.82 \%$ to $4.77 \%$ and a reduction in false alarm rates by $\mathbf{4. 9 \%}$ to $\mathbf{2 3. 1 8 \%}$.

Continual relation extraction (RE) aims to learn constantly emerging relations while avoiding forgetting the learned relations.Existing works store a small number of typical samples to re-train the model for alleviating forgetting.However, repeatedly replaying these samples may cause the overfitting problem.We conduct an empirical study on existing works and observe that their performance is severely affected by analogous relations.To address this issue, we propose a novel continual extraction model for analogous relations.Specifically, we design memory-insensitive relation prototypes and memory augmentation to overcome the overfitting problem.We also introduce integrated training and focal knowledge distillation to enhance the performance on analogous relations.Experimental results show the superiority of our model and demonstrate its effectiveness in distinguishing analogous relations and overcoming overfitting.