# Proxy-Based Semi-Supervised Cross-Modal Hashing
## 4 Experiments
### Experimental Settings  
Datasets. We evaluated our method on three widely used benchmark datasets: MIRFLICKR-25K [44], NUS-WIDE [45], and MS COCO [46]. A brief description of each is provided below:  
* MIRFLICKR-25K. This small-scale cross-modal dataset consists of 24,581 image-text pairs, spanning 24 categories, with each sample belonging to at least one category. * NUS-WIDE. Comprising 269,648 image-text pairs, this dataset includes 81 categories. We filtered out categories with fewer samples and selected 21 common categories, resulting in 195,834 image-text pairs. * MS COCO. A large-scale dataset commonly used in computer vision, containing 82,785 training images and 40,504 validation images. Each image is associated with textual descriptions and labels across 80 categories. For our experiments, we combined the training and validation sets, with each sample belonging to at least one of these categories. Implementation Details. To facilitate comparison, we follow the settings of the baseline method. The **threshold** was set to 0.5, **epoch num** was set to 100, we applied the same procedure across the three public datasets, and we randomly selected 10,000 samples as the training set, which were then divided into labeled and unlabeled data according to a certain ratio. In addition, we randomly selected 5000 samples as the query set, with the remaining samples being used as the database. In this process, images are resized to 224 \(\times\) 224, and text is represented using BPE encoding. Experimental Environment. We implemented our PSSCH method using PyTorch==1.12.1, with an NVIDIA RTX 3090 GPU. The batch size was set to 128, with 80 labeled samples and 48 unlabeled samples. The hyper-parameters \(\alpha\) and \(\beta\) were set to 0.5 and 1, respectively. We used two Transformer encoders as the backbone for the PSSCH method: ViT [41] for the image and GPT-2 [47] for the text. The backbone network parameters were initialized using pre-trained CLIP features (ViT-B/32) [48]. The parameters of ImgNet and TxtNet were updated using the Adam optimizer, with a learning rate of 0.00001 for the backbone and 0.001 for the hashing layer. The CPNet network parameters were updated using the SGD optimizer with a learning rate of 0.001. Baseline Methods. We selected 10 excellent deep cross-modal hashing methods for comparison, including four supervised cross-modal hashing methods, i.e., LEMON [49], EDMH [50], HCCH [51], and HMAH [52], four semi-supervised cross-modal hashing methods, i.e., SSCH [22], MGCH [19], TS3H [14], and GCSCH [20], and two unsupervised cross-modal hashing methods, i.e., DGCPN [53] and UCCH [54]. For supervised methods, we train using only the labeled data. Due to some methods not being open source, we directly cite the results from the published papers. Here is a brief introduction to each baseline:  
* LEMON. embeds label information into the hash learning process in order to fully utilize the semantic information of labels to guide the learning of hash functions. * EDMH. proposes a discrete optimization algorithm that seamlessly integrates three useful discrete constraints into a joint hashing learning model. * HCCH. mitigates the loss of important discriminative information, a hierarchical hashing scheme from coarse to fine is proposed, which refines useful discriminative information step by step using a two-layer hashing function. * HMAH. creates a hierarchical message aggregation network within a teacher-student framework, enhancing alignment of heterogeneous modalities and modeling detailed cross-modal correlations. * SSCH. obtains enhanced semantic information through a pseudo-labeling process that does not require alignment and learns the hash representations of various data via a label enhancement strategy. * MGCH. employs a multi-view graph to connect the data, utilizing anchor points as a unified semantic hub to achieve semi-supervised cross-modal hashing. * TS3H.