# Supervised Contrastive Learning for 3D Cross-Modal Retrieval
## 2 Related Works
### Cross-Domain and -Modal Feature Learning  
Cross-domain adaptation involves extracting shared, common features across different domains, making its task similar to cross-modal approaches. Various methods have been proposed to extract and utilize features in the cross-domain environments. [PERSON] et al. proposed a method to train on labeled and unlabeled data from the target domain while preserving as much structural information from the source domain as possible [15]. Feature distributions in the source domain were analyzed to extract features from the target domain consisting of labeled and unlabeled data while preserving its structural similarity. [PERSON] et al. aimed to train on unlabeled data in the target domain using existing data from the source domain [16]. They proposed a method that utilizes the original backbone model, the source domain fine-tuned model, and the source and target domain fine-tuned model to extract features and learn to cluster these features by class in the latent space. Cross-modal retrieval tasks have been continuously studied in the past. The most challenging aspect of cross-modal retrieval is the imbalanced feature distribution caused by the differences between modalities. Ultimately, it all boils down to reducing the heterogeneous gap between modalities and contextually integrating the features. The initial supervised cross-modal retrieval methods have been mainly studied for image-text or image-point [5; 6; 7; 17; 18; 19]. These studies aimed to address the aforementioned issues by performing contextual adjustments that can extract discriminative features through semantic label prediction, displaying similar feature distribution across different modalities. Simultaneously, they tried to decrease the distance of inter-modal embedding features, enabling fine-tuned adjustments at the backbone level. As a result, they were successful in representing similar features for the same object class across different modalities. Among these methods, Deep Supervised Cross-Modal Retrieval (DSCMR) mitigates the heterogeneous gap between text and image modalities by acquiring and comparing features in both label and embedding spaces [7]. Furthermore, [PERSON] et al. proposed a method named cross-modal center loss (CLF) which established anchor features called cross-modal center features to generate distinct feature clustering across multi-modal environments [3]. As a result, it led to the convergence of all features from each modality, exhibiting robust feature clustering against modal variance. Later, RONO further expanded CLF by including label noise [2]. RONO enhances distinguishability using noisy labels, creating features that are robust against various changes during evaluation. Previous approaches, CLF and RONO, aim to extract discriminative features and to represent modal coherent features in cross-modal environments to carry out 3D object retrieval tasks using label prediction and cross-modal center loss. For label prediction, feature projection was employed to obtain contextual feature similarity. However,since the projection head is not used in object retrieval tasks, updating the projection head through backpropagation may lead to retrieval performance degradation. Furthermore, the cross-modal center loss used to address inter-class and inter-modal variance relies heavily on center features that may become biased depending on the hyperparameters. Therefore, this paper proposes a new method for 3D object retrieval using both supervised and contrastive learning in a cross-modal environment.