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 $\mathbf{2 3. 1 8 \%}$.

Converting webpage designs into code (design-to-code) plays a vital role in User Interface (UI) development for front-end developers, bridging the gap between visual design and functional implementation. While recent Multimodal Large Language Models (MLLMs) have shown significant potential in design-to-code tasks, they often fail to accurately preserve the layout during code generation. To this end, we draw inspiration from the Chain-of-Thought (CoT) reasoning in human cognition and propose LaTCoder, a novel approach that enhances layout preservation in webpage design during code generation with Layout-as-Thought (LaT). Specifically, we first introduce a simple yet efficient algorithm to divide the webpage design into image blocks. Next, we prompt MLLMs using a CoT-based approach to generate code for each block. Finally, we apply two assembly strategies-absolute positioning and an MLLM-based method-followed by dynamic selection to determine the optimal output. We evaluate the effectiveness of LaTCoder using multiple backbone MLLMs (i.e., DeepSeek-VL2, Gemini, and GPT-4o) on both a public benchmark and a newly introduced, more challenging benchmark (CC-HARD) that features complex layouts. The experimental results on automatic metrics demonstrate significant improvements. Specifically, TreeBLEU scores increased by 66.67% and MAE decreased by 38% when using DeepSeek-VL2, compared to direct prompting. Moreover, the human preference evaluation results indicate that annotators favor the webpages generated by LaTCoder in over 60% of cases, providing strong evidence of the effectiveness of our method.

Fault detection (FD) is essential for wafer quality control in semiconductor manufacturing (SM), as it can identify abnormal wafers in the early stages. However, frequent chamber discrepancies leads to distribution shifts in the data from different chambers, which causes performance degradation of the existing model. In this paper, we conceive a robust cross-chamber fault detection method in SM, which to the best of our knowledge is the first work that employs domain generalization to address the issue of cross-chamber fault detection in SM. Our basic idea is to map the samples from the source chambers and target chambers to the same hypersphere space, making normal samples cluster around a specific feature center while faulty samples stay away from it. Due to the scarcity of faulty samples, we propose a one-class classification-based fault detection method relying on normal samples to establish classification boundaries. To generalize the model to unseen target chambers, we design a meta-learning-based one-class domain generalization approach. We also devise a strategy to enhance distribution alignment within hypersphere, making the classification boundaries of various chambers be close to each other. The evaluations on real-world data collected from a wet process equipment in SM verify the high robustness of the model to chamber discrepancies with limited faulty samples.

Since the standard license plate of large vehicle is easily affected by occlusion and stain, the traffic management department introduces the enlarged license plate at the rear of the large vehicle to assist license plate recognition. However, current researches regards standard license plate recognition and enlarged license plate recognition as independent tasks, and do not take advantage of the complementary benefits from the two types of license plates. In this work, we propose a new computer vision task called collaborative license plate recognition, aiming to leverage the complementary advantages of standard and enlarged license plates for achieving more accurate license plate recognition. To achieve this goal, we propose an Association Enhancement Network (AENet), which achieves robust collaborative licence plate recognition by capturing the correlations between characters within a single licence plate and enhancing the associations between two license plates. In particular, we design an association enhancement branch, which supervises the fusion of two licence plate information using the complete licence plate number to mine the association between them. To enhance the representation ability of each type of licence plates, we design an auxiliary learning branch in the training stage, which supervises the learning of individual license plates in the association enhancement between two license plates. In addition, we contribute a comprehensive benchmark dataset called CLPR, which consists of a total of 19,782 standard and enlarged licence plates from 24 provinces in China and covers most of the challenges in real scenarios, for collaborative license plate recognition. Extensive experiments on the proposed CLPR dataset demonstrate the effectiveness of the proposed AENet against several state-of-the-art methods.