The Solution for the CVPR 2023 1st foundation model challenge-Track2

In this paper, we propose a solution for cross-modal transportation retrieval. Due to the cross-domain problem of traffic images, we divide the problem into two sub-tasks of pedestrian retrieval and vehicle retrieval through a simple strategy. In pedestrian retrieval tasks, we use IRRA as the base model and specifically design an Attribute Classification to mine the knowledge implied by attribute labels. More importantly, We use the strategy of Inclusion Relation Matching to make the image-text pairs with inclusion relation have similar representation in the feature space. For the vehicle retrieval task, we use BLIP as the base model. Since aligning the color attributes of vehicles is challenging, we introduce attribute-based object detection techniques to add color patch blocks to vehicle images for color data augmentation. This serves as strong prior information, helping the model perform the image-text alignment. At the same time, we incorporate labeled attributes into the image-text alignment loss to learn fine-grained alignment and prevent similar images and texts from being incorrectly separated. Our approach ranked first in the final B-board test with a score of 70.9.

Rethinking Boundary Discontinuity Problem for Oriented Object Detection

Oriented object detection has been developed rapidly in the past few years, where rotation equivariant is crucial for detectors to predict rotated bounding boxes. It is expected that the prediction can maintain the corresponding rotation when objects rotate, but severe mutational in angular prediction is sometimes observed when objects rotate near the boundary angle, which is well-known boundary discontinuity problem. The problem has been long believed to be caused by the sharp loss increase at the angular boundary during training, and widely used IoU-like loss generally deal with this problem by loss-smoothing. However, we experimentally find that even state-of-the-art IoU-like methods do not actually solve the problem. On further analysis, we find the essential cause of the problem lies at discontinuous angular ground-truth(box), not just discontinuous loss. There always exists an irreparable gap between continuous model ouput and discontinuous angular ground-truth, so angular prediction near the breakpoints becomes highly unstable, which cannot be eliminated just by loss-smoothing in IoU-like methods. To thoroughly solve this problem, we propose a simple and effective Angle Correct Module (ACM) based on polar coordinate decomposition. ACM can be easily plugged into the workflow of oriented object detectors to repair angular prediction. It converts the smooth value of the model output into sawtooth angular value, and then IoU-like loss can fully release their potential. Extensive experiments on multiple datasets show that whether Gaussian-based or SkewIoU methods are improved to the same performance of AP50 and AP75 with the enhancement of ACM.

Every Corporation Owns Its Structure: Corporate Credit Ratings via Graph Neural Networks

Credit rating is an analysis of the credit risks associated with a corporation, which reflects the level of the riskiness and reliability in investing, and plays a vital role in financial risk. There have emerged many studies that implement machine learning and deep learning techniques which are based on vector space to deal with corporate credit rating. Recently, considering the relations among enterprises such as loan guarantee network, some graph-based models are applied in this field with the advent of graph neural networks. But these existing models build networks between corporations without taking the internal feature interactions into account. In this paper, to overcome such problems, we propose a novel model, Corporate Credit Rating via Graph Neural Networks, CCR-GNN for brevity. We firstly construct individual graphs for each corporation based on self-outer product and then use GNN to model the feature interaction explicitly, which includes both local and global information. Extensive experiments conducted on the Chinese public-listed corporate rating dataset, prove that CCR-GNN outperforms the state-of-the-art methods consistently.