6 accepted papers
Infrared video acquisition inherently suffers from low spatial resolution and limited frame rates due to the physical constraints of thermal imaging sensors. These limitations make infrared video enhancement uniquely challenging, as it requires restoring spatial details and temporal continuity from
Pretrained vision-language models (VLMs), e.g., CLIP, demonstrate impressive zero-shot capabilities on downstream tasks. Prior research highlights the crucial role of visual augmentation techniques, like random cropping, in alignment with fine-grained class descriptions generated by large language m…
Large-scale pretrained models have proven immensely valuable in handling data-intensive modalities like text and image. However, fine-tuning these models for certain specialized modalities, such as protein sequence and cosmic ray, poses challenges due to the significant modality discrepancy and scar…
Cross-modality transfer aims to leverage large pretrained models to complete tasks that may not belong to the modality of pretraining data. Existing works achieve certain success in extending classical finetuning to cross-modal scenarios, yet we still lack understanding about the influence of modali…
The development of vision models for real-world applications is hindered by the challenge of annotated data scarcity, which has necessitated the adoption of data-efficient visual learning techniques such as semi-supervised learning. Unfortunately, the prevalent cross-entropy supervision is limited b…
Developing generalizable models that can effectively learn from limited data and with minimal reliance on human supervision is a significant objective within the machine learning community, particularly in the era of deep neural networks. Therefore, to achieve data-efficient learning, researchers ty…