We observed that Contrastive Language-Image Pretraining (CLIP) models struggle with real-world downstream tasks such as road traffic anomaly detection, due to their inability to effectively capture spatial and action relationships between objects within images. To address this, we compile and curate a dataset with 1M samples of images using language supervision provided by the common image caption dataset, in which each image is paired with subject-relationship-object descriptions emphasizing spatial and action interactions, and train a Spatial and Action relationship aware CLIP (SA-CLIP) model. We evaluated the proposed model on the Visual Spatial Reasoning (VSR) dataset and further verified its effectiveness on the Detection-of-Traffic-Anomaly (DoTA) dataset. Experiment results show that the proposed SA-CLIP demonstrates strong abilities in understanding spatial relationships while achieving good zero-shot performance on the traffic anomaly detection task.

Recent studies improve the cross-lingual transfer learning by better aligning the internal representations within the multilingual model or exploring the information of the target language using self-training. However, the alignment-based methods exhibit intrinsic limitations such as non-transferable linguistic elements, while most of the self-training based methods ignore the useful information hidden in the low-confidence samples. To address this issue, we propose CoNLST (Contrastive Negative Learning and Self-Training) to leverage the information of low-confidence samples. Specifically, we extend the negative learning to the metric space by selecting negative pairs based on the complementary labels and then employ self-training to iteratively train the model to converge on the obtained clean pseudo-labels. We evaluate our approach on the widely-adopted cross-lingual benchmark XNLI. The experiment results show that our method improves upon the baseline models and can serve as a beneficial complement to the alignment-based methods.