Zeping Li, Xinlong Yang, Ziheng Gao, Ji Liu, Guanchen Li, Zhuang Liu, Dong Li, Jinzhang Peng, Lu Tian, Emad Barsoum. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

As an important problem in searching system development, domain adaptive retrieval seeks to train a retrieval model with both labeled source samples and unlabeled target samples. Although several domain adaptive hashing algorithms have been proposed to handle the problem with high efficiency, they often presume that source and target domains share all classes. However, prior knowledge about the label space on the target domain is hard to obtain in reality. To tackle this, we study a novel and challenging problem of universal domain adaptive retrieval, which evidently increases the difficulty of effective domain alignment. In this paper, we propose a hashing method namedRelational and prOtotypicalStructure lEarning (ROSE) to solve the problem. In particular, to overcome domain shift, we construct a relational structure depicting cross-domain similar pairs based on ranking statistics, then learn from the structure by maximizing the similarity between similar pairs compared with challenging negatives. Moreover, target private samples are detected using the min-max criterion, which helps to construct hashing prototypes in the Hamming space. On this basis, we combine prototypical structure learning with online clustering in the Hamming space, which improves target semantic learning under label deficiency. Extensive experiments on several benchmarks demonstrate that our proposed ROSE significantly outperforms a wide range of state-of-the-art methods. Our source code is available athttps://github.com/WillDreamer/Rose.git.

Hashing aims to compress raw data into compact binary descriptors, which has drawn increasing interest for efficient large-scale image retrieval. Current deep hashing often employs evaluation protocols where usually query data and training data are from similar distributions. However, more realistic evaluations should take into account a broad spectrum of distribution shifts with varying degrees. Therefore, we study the problem of out-of-distribution generalization in image retrieval, which seeks to learn a retrieval model from a source domain and generalize to unseen target domains. However, this problem is challenging owing to data scarcity in target domains and the potential overfitting of domain-specific patterns. Here, we propose a novel hashing model namedLooking-into-gradients (LOG) for image retrieval under out-of-distribution shifts, which comprehensively explores gradients for both data generation and model optimization. Specifically, to overcome data deficiency in target domains, we formalize the worst-case problem to generate challenging virtue samples via adversarial gradient ascend. Besides, to further enhance model generalization capability, we not only identify non-crucial parameters with minor gradients and values and shrink them to zero, but also modify the inconsistent gradients across domains to prevent learning domain-specific patterns. Extensive experiments on various datasets demonstrate that LOG outperforms state-of-the-art methods by up to 8.54%.

Label noise is pervasive in real-world applications, which influences the optimization of neural network models. This paper investigates a realistic but understudied problem of image retrieval under label noise, which could lead to severe overfitting or memorization of noisy samples during optimization. Moreover, identifying noisy samples correctly is still a challenging problem for retrieval models. In this paper, we propose a novel approach called Prototypical Mixing and Retrieval-based Refinement (TITAN) for label noise-resistant image retrieval, which corrects label noise and mitigates the effects of the memorization simultaneously. Specifically, we first characterize numerous prototypes with Gaussian distributions in the hidden space, which would direct the Mixing procedure in providing synthesized samples. These samples are fed into a similarity learning framework with varying emphasis based on the prototypical structure to learn semantics with reduced overfitting. In addition, we retrieve comparable samples for each prototype from simple to complex, which refine noisy samples in an accurate and class-balanced manner. Comprehensive experiments on five benchmark datasets demonstrate the superiority of our proposed TITAN compared with various competing baselines.

Driven by the progress of large-scale pre-training, parameter-efficient transfer learning has gained immense popularity across different subfields of Artificial Intelligence. The core is to adapt the model to downstream tasks with only a small set of parameters. Recently, researchers have leveraged such proven techniques in multimodal tasks and achieve promising results. However, two critical issues remain unresolved: how to further reduce the complexity with lightweight design and how to boost alignment between modalities under extremely low parameters. In this paper, we propose A gracefUl pRompt framewOrk for cRoss-modal trAnsfer (AURORA) to overcome these challenges. Considering the redundancy in existing architectures, we first utilize the mode approximation to generate 0.1M trainable parameters to implement the multimodal parameter-efficient tuning, which explores the low intrinsic dimension with only 0.04% parameters of the pre-trained model. Then, for better modality alignment, we propose the Informative Context Enhancement and Gated Query Transformation module under extremely few parameters scenes. A thorough evaluation on six cross-modal benchmarks shows that it not only outperforms the state-of-the-art but even outperforms the full fine-tuning approach. Our code is available at: https://github.com/WillDreamer/Aurora.