There is no benefit that artificial intelligence has developed very much in many fields. However, without taking into account the generalization and adaptability of AI models for a particular domain, an accurate assessment of their progress is incomplete. Domain adaptation (DA) and domain generalization (DG) have attracted ample attention from researchers around the world. Training is an in-depth process and given that the world recognizes the rarity of “good” data, it is essential that models trained in limited source domains work well in new fields. A considerable amount of research has been conducted in DA and DG. . However, most of this study is based on unimodal data such as images and time series. With the advent of large multimodal datasets, researchers are currently striving to find solutions that address multimodal domain adaptation (MMDA) and generalization (MMDG) across multiple modalities. This article provides a comprehensive overview of recent advances in MMDA and MMDG, from the traditional vanilla approach to the use of basic models.
Researchers from Zurich, ETH, TUM, and Germany have published a comprehensive and thorough study of advances in multimodal adaptation and generalization, together with others. This study details the issue statement, challenges, datasets, applications, work still underway, and future directions for the following five topics:
(1) Multimodal domain adaptation -: The objective is to improve domain cross-knowledge forwarding, i.e., training the model on a labeled source domain, while still in unlabeled target domain despite distribution shifts. It is to ensure that it adapts effectively. Researchers have struggled with the distinct properties of various modalities and how to combine them. Furthermore, there are many cases where input between modalities is missing.
To address this issue, researchers have addressed many aspects, including hostile learning, control learning, and cross-modal interaction techniques. Some important works in this field are the frameworks of MM-Sada and Xmuda.
(2) Unlike MMDA, which adapts models before deployment, multimodal test time adaptation – Multimodal test time adaptation (MMTTA) focuses on the ability of models to self-regulate during inference without the need for labeled data I’m guessing it. The main obstacle in this direction is the rarity of source domain data. Furthermore, if the model requires retry every time, continuous distribution shifts of the data will not work. Researchers use self-teacher learning and uncertainty estimation techniques to solve this problem. Some notable contributions in this field are reads (reliability-aware attention distribution) and adaptive entropy optimization (AEO).
(3) Multimodal Domain Generalization: Multimodal Domain Generalization (MMDG) aims to train AI models that can generalize to completely new domains without prior exposure. Like the previous two, the lack of target domain data during training also creates problems for this purpose. Furthermore, inconsistencies in functional distributions make it difficult for the model to learn domain-invariant representations. This fieldwork is carried out using algorithms such as SIMMMMDG, Moosa, and other in dismantling of functions and cross-modal knowledge transfer.
(4) Domain adaptation and generalization with the help of multimodal foundation models: This section mainly discusses the rise of basic models such as Clip in improving DA and DG. The basic model is pre-trained and has a rich understanding of the diverse modalities, making it a suitable candidate. These models appear to be perfect solutions to all the problems mentioned above, but their use remains difficult due to high computational requirements and adaptability constraints. To address this issue, researchers propose elegant methods such as functional space enhancement, knowledge distillation, and synthetic data generation, through contributions such as enhancement of clip-based features and diffusion-driven synthetic data generation. I’m doing it.
(5) Adaptation of multimodal foundation models: This subtopic deals with the problems of the basic model fine-tuned for adaptation purposes. Researchers have proposed methods such as rapid learning and adapter-based tuning to combat computational costs and shortages of domain data. Some of the most notable works of recent are the first COOP and COCOOP, and COOP and COCOPTE for the latter method.
Conclusion: This article discussed the issues of generalizability and adaptability in multimodal applications. We have seen many subdomains of this field of research and a range of works ranging from naive augmentation approaches to basic models that solve challenges. Furthermore, this research paper presents all relevant information and highlights the scope of future work to develop a more efficient and robust framework and self-learning model.
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Adeeba Alam Ansari currently holds a dual degree from the Indian Institute of Technology (IIT) Kharagpur, earning B.Tech in Industrial Engineering and M.Tech in Financial Engineering. With a strong interest in machine learning and artificial intelligence, she is a passionate reader and a curious individual. Adeeba is a firm believer in the power of technology to empower society and promote welfare through innovative solutions based on empathy and a deep understanding of real-world challenges.
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