Recent advances in model compression have highlighted the potential of low-bit precision techniques, with Binary Neural Networks (BNNs) attracting attention for their extreme efficiency. However, extreme quantization in BNNs limits representational capacity and destabilizes training, posing significant challenges for lightweight architectures with depth-wise convolutions. To address this, we propose a 1.58-bit convolution to enhance expressiveness and a pre-BN residual connection to stabilize optimization by improving the Hessian condition number. These innovations enable, to the best of our knowledge, the first successful binarization of depth-wise convolutions in BNNs. Our method achieves 33M OPs on ImageNet with MobileNet V1, establishing a new state-of-the-art in BNNs by outperforming prior methods with comparable OPs. Moreover, it consistently outperforms existing methods across various datasets, including CIFAR-10, CIFAR-100, STL-10, Tiny ImageNet, and Oxford Flowers 102, with accuracy improvements of up to 9.3 percentage points.

Self-supervised learning (SSL) methods based on the instance discrimination tasks with InfoNCE have achieved remarkable success. Despite their success, SSL models often struggle to generate effective representations for unseen-domain data. To address this issue, research on unsupervised domain generalization (UDG), which aims to develop SSL models that can generate domain-irrelevant features, has been conducted. Most UDG approaches utilize contrastive learning with InfoNCE to generate representations, and perform feature alignment based on strong assumptions to generalize domain-irrelevant common features from multi-source domains. However, existing methods that rely on instance discrimination tasks are not effective at extracting domain-irrelevant common features. This leads to the suppression of domain-irrelevant common features and the amplification of domain-relevant features, thereby hindering domain generalization. Furthermore, strong assumptions underlying feature alignment can lead to biased feature learning, reducing the diversity of common features. In this paper, we propose a novel approach, DomCLP, Domain-wise Contrastive Learning with Prototype Mixup. We explore how InfoNCE suppresses domain-irrelevant common features and amplifies domain-relevant features. Based on this analysis, we propose Domain-wise Contrastive Learning (DCon) to enhance domain-irrelevant common features. We also propose Prototype Mixup Learning (PMix) to generalize domain-irrelevant common features across multiple domains without relying on strong assumptions. The proposed method consistently outperforms state-of-the-art methods on the PACS and DomainNet datasets across various label fractions, showing significant improvements.

Text-to-SQL, which translates a natural language question into an SQL query, has advanced with in-context learning of Large Language Models (LLMs). However, existing methods show little improvement in performance compared to randomly chosen demonstrations, and significant performance drops when smaller LLMs (e.g., Llama 3.1-8B) are used. This indicates that these methods heavily rely on the intrinsic capabilities of hyper-scaled LLMs, rather than effectively retrieving useful demonstrations. In this paper, we propose a novel approach for effectively retrieving demonstrations and generating SQL queries. We construct a Deep Contextual Schema Link Graph, which contains key information and semantic relationship between a question and its database schema items. This graph-based structure enables effective representation of Text-to-SQL samples and retrieval of useful demonstrations for in-context learning. Experimental results on the Spider benchmark demonstrate the effectiveness of our approach, showing consistent improvements in SQL generation performance and efficiency across both hyper-scaled LLMs and small LLMs. The code is available at https://github.com/jjklle/DCG-SQL.

Weakly-supervised Semantic Segmentation (WSSS) aims to provide a precise semantic segmentation results without expensive pixel-wise segmentation labels. With the supervision gap between classification and segmentation, Image-level WSSS mainly relies on Class Activation Maps (CAMs) from the classification model to emulate the pixel-wise annotations. However, CAMs often fail to cover the entire object region because classification models tend to focus on narrow discriminative regions in an object. Towards accurate CAM coverage, Existing WSSS methods have tried to boost feature representation learning or impose consistency regularization to the classification models, but still there are limitation in activating non-discriminative area, where the focus of the models is weak. To tackle this issue, we propose FSAE framework, which provides explicit supervision of non-discriminative area, encouraging the CAMs to activate on various object features. We leverage weak-strong consistency with pseudo-label expansion strategy for reliable supervision and enhance learning of non-discriminative object boundaries. Specifically, we use strong perturbation to make challenging inference target, and focus on generating reliable pixel-wise supervision signal for broad object regions. Extensive experiments on the WSSS benchmark datasets show that our method boosts initial seed quality and segmentation performance by large margin, achieving new state-of-the-art performance on benchmark WSSS datasets. Our public code is available at https://github.com/obeychoi0120/FSAE.

Deep Neural Networks (DNNs) have demonstrated remarkable performance across diverse domains and tasks with large-scale datasets. To reduce labeling costs for large-scale datasets, semi-automated and crowdsourcing labeling methods are developed, but their labels are in-evitably noisy. Learning with Noisy Labels (LNL) approaches aim to train DNNs despite the presence of noisy labels. These approaches utilize the memorization effect to select correct labels and refine noisy ones, which are then used for subsequent training. However, these methods en-counter a significant decrease in the model's generalization performance due to the inevitably existing noise labels. To overcome this limitation, we propose a new approach to enhance learning with noisy labels by incorporating additional distribution informationstructural labels. In order to leverage additional distribution information for generalization, we employ a reverse k-NN, which helps the model in achieving a better feature manifold and mitigating over-fitting to noisy labels. The proposed method shows outperformed performance in multiple benchmark datasets with IDN and real-world noisy datasets.