# Gaze-Dependent Image Re-Ranking Technique for Enhancing Content-Based Image Retrieval
## 2 Related Works
### Re-Ranking Method  
In the domain of image retrieval, re-ranking is an essential component that entails the precise re-ordering of the initial retrieval results. Re-ranking is a crucial aspect of various retrieval tasks, including object retrieval, person re-identification, and CBIR, due to its ability to improve the accuracy and relevance of the retrieved results. Two distinct types of re-ranking approaches exist, namely self-re-ranking and interactive re-ranking.  
Self-re-ranking is an automated technique that re-orders the initial retrieval results using data derived from the top-ranked images in the retrieval results. Certain retrieval approaches [39; 40; 41] employ text labels associated with top-ranked images as supplementary data to calculate the correlation between the query image and candidate images, thereby facilitating the re-ranking process. In contrast, [PERSON] et al. [42] utilized the class information of the top-ranked images during the re-ranking stage. The above techniques can enhance retrieval efficacy without requiring input from users. However, in the absence of supplemental information to bridge the discrepancy in comprehension between users and models, these methods still require assistance in disambiguating inherently vague queries. In contrast to the above techniques, our method bridges the semantic gap by incorporating gaze information to anticipate the user's preference.  
Interactive re-ranking is a technique that involves re-ordering the initial retrieval results by interacting with users, typically through feedback mechanisms such as relevance feedback or users' preferences. Traditional interactive re-ranking techniques [43; 44; 45; 46; 47] entail users selecting images associated with the desired image from the initial retrieval results, after which the retrieved results are re-ordered based on the feedback obtained from users. In the fashion industry, novel approaches [48; 49] have been proposed as alternatives for obtaining feedback, employing natural language to assess the top-ranked images. These approaches deviate from the conventional feedback mechanisms and provide a fresh outlook on evaluating fashion items. [PERSON] et al. [50] propose a method that utilizes text feedbacks to improve image retrieval accuracy by incorporating multi-grained uncertainty regularization to handle the complex relationship between the image and text features. Re-ranking can be performed using reinforcement learning or deep metric learning, depending on the feedback received in a natural language format. [PERSON] et al. [51] introduced an additional technique for efficiently handling multiple iterations of user-provided natural language queries to reorganize the retrieved results. Generally, interactive re-ranking uses users' diverse feedback to obtain re-ranking information. Furthermore, to better explore user preferences and bridge the semantic gap in CBIR, what type of information is suitable to be introduced as feedback while interacting with users is a topic worthy of in-depth exploration and long-term research.