Recently, the accuracy of image-text matching has been greatly improved by multimodal pretrained models, all of which use millions or billions of paired images and texts for supervised model learning. Different from them, human brains can well match images with texts using their stored multimodal knowledge. Inspired by that, this paper studies a new scenario as unpaired image-text matching, in which paired images and texts are assumed to be unavailable during model learning. To deal with it, we accordingly propose a simple yet effective method namely Multimodal Aligned Conceptual Knowledge (MACK). First, we collect a set of words and their related image regions from publicly available datasets, and compute prototypical region representations to obtain pretrained general knowledge. To make the obtained knowledge better suit for certain datasets, we refine it using unpaired images and texts in a self-supervised learning manner to obtain fine-tuned domain knowledge. Then, to match given images with texts based on the knowledge, we represent parsed words in the texts by prototypical region representations, and compute region-word similarity scores. At last, the scores are aggregated based on bidirectional similarity pooling into an image-text similarity score, which can be directly used for unpaired image-text matching. The proposed MACK is complementary with existing models, which can be easily extended as a re-ranking method to substantially improve their performance of zero-shot and cross-dataset image-text matching.

The stock trend prediction problem refers to forecasting future stock price trends. In recent years, some methods discovered causal relations between stocks to address this problem. However, traditional causal discovery methods face unique challenges in the stock market, as they fail to uncover accurate causal relationships when a distribution shift happens in stock. Additionally, current methods also overlook the commonalities and differences between stock relations. To address these shortcomings, we propose a causal-enhanced multi-view temporal graph model, named CMG. This method explores comprehensive causal relations by incorporating distribution shift confounder and constructs a multi-view contrastive learning module to unearth the commonalities and differences between stock relations, thereby enabling more accurate stock trend predictions. Further experimental results and investment simulations demonstrate the effectiveness and profitability of CMG.

Pre-trained vision-language models (VLMs) have achieved high performance on various downstream tasks, which have been widely used for visual grounding tasks in a weakly supervised manner. However, despite the per-formance gains contributed by large vision and language pre-training, we find that state-of-the-art VLMs struggle with compositional reasoning on grounding tasks. To demonstrate this, we propose Attribute, Relation, and Pri-ority grounding (ARPGrounding) benchmark to test VLMs' compositional reasoning ability on visual grounding tasks. ARPGrounding contains 11,425 samples and evaluates the compositional understanding of VLMs in three dimensions: 1) attribute, denoting comprehension of objects' properties; 2) relation, indicating an understanding of relation between objects; 3) priority, reflecting an awareness of the part of speech associated with nouns. Using the ARPGrounding benchmark, we evaluate several mainstream VLMs. We empirically find that these models perform quite well on conventional visual grounding datasets, achieving performance comparable to or surpassing state-of-the-art methods but showing strong deficiencies in compositional reasoning. Furthermore, we propose a composition-aware fine-tuning pipeline, demonstrating the potential to lever-age cost-effective image-text annotations for enhancing the compositional understanding of VLMs in grounding tasks. Code is available at link.

Recently, the accuracy of image-text matching has been greatly improved by multimodal pretrained models, all of which are trained on millions or billions of paired images and texts. Different from them, this paper studies a new scenario as unpaired image-text matching, in which paired images and texts are assumed to be unavailable during model training. To deal with this, we propose a simple yet effective method namely Multimodal Aligned Conceptual Knowledge (MACK), which is inspired by the knowledge use in human brain. It can be directly used as general knowledge to correlate images and texts even without model training, or further fine-tuned based on unpaired images and texts to better generalize to certain datasets. In addition, we extend it as a re-ranking method, which can be easily combined with existing image-text matching models to substantially improve their performance.