Abstract
Unsupervised domain adaptation remains a critical challenge in enabling the knowledge transfer of models across unseen domains. Existing methods struggle to balance the need for domain-invariant representations with preserving domain-specific features, which is often due to alignment approaches that impose the projection of samples with similar semantics close in the latent space despite their drastic domain differences. We introduce LAGUNA - LAnguage Guided UNsupervised Adaptation with structured spaces, a novel approach that shifts the focus from aligning representations in absolute coordinates to aligning the relative positioning of equivalent concepts in latent spaces. LAGUNA defines a domain-agnostic structure upon the semantic/geometric relationships between class labels in language space and guides adaptation, ensuring that the organization of samples in visual space reflects reference inter-class relationships while preserving domain-specific characteristics. We empirically demonstrate LAGUNA’s superiority in domain adaptation tasks across four diverse images and video datasets. Remarkably, LAGUNA surpasses previous works in 18 different adaptation scenarios across four diverse image and video datasets with average accuracy improvements of +3.32% on DomainNet, +5.75% in GeoPlaces, +4.77% on GeoImnet, and +1.94% mean class accuracy improvement on EgoExo4D.
Anxhelo Diko
PhD Student In Computer Science
A highly motivated and results-oriented Computer Vision Ph.D. student with a deep passion for advancing the field of artificial intelligence. My research focuses on building multimodal representations and understanding human activities, addressing key challenges for autonomous agents and AI in general. I have extensive experience with multimodal large language models for video captioning and question answering and a keen interest in view-invariant video representation learning. I am particularly committed to exploring how to effectively bridge the gap between representations of different modalities while preserving their unique characteristics. In addition to my research expertise, I have a strong engineering foundation honed through academic and industry experiences. Proficient in Python, C++, and CUDA, I excel at rapidly prototyping and implementing innovative ideas. I am eager to leverage my skills and knowledge to contribute to cutting-edge research and development in this dynamic field.