Semi-Supervised Domain Generalization for Object Detection via Language-Guided Feature Alignment

Existing domain adaptation (DA) and generalization (DG) methods in object detection enforce feature alignment in the visual space but face challenges like object appearance variability and scene complexity, which make it difficult to distinguish between objects and achieve accurate detection. In this paper, we are the first to address the problem of semi-supervised domain generalization by exploring vision-language pre-training and enforcing feature alignment through the language space. We employ a novel Cross-Domain Descriptive Multi-Scale Learning (CDDMSL) aiming to maximize the agreement between descriptions of an image presented with different domain-specific characteristics in the embedding space. CDDMSL significantly outperforms existing methods, achieving 11.7% and 7.5% improvement in DG and DA settings, respectively. Comprehensive analysis and ablation studies confirm the effectiveness of our method, positioning CDDMSL as a promising approach for domain generalization in object detection tasks.Comment: Accepted at BMVC 202

Incorporating Geo-Diverse Knowledge into Prompting for Increased Geographical Robustness in Object Recognition

Existing object recognition models have been shown to lack robustness in diverse geographical scenarios due to domain shifts in design and context. Class representations need to be adapted to more accurately reflect an object concept under these shifts. In the absence of training data from target geographies, we hypothesize that geographically diverse descriptive knowledge of categories can enhance robustness. For this purpose, we explore the feasibility of probing a large language model for geography-based object knowledge, and we examine the effects of integrating knowledge into zero-shot and learnable soft prompting with CLIP. Within this exploration, we propose geography knowledge regularization to ensure that soft prompts trained on a source set of geographies generalize to an unseen target set. Accuracy gains over prompting baselines on DollarStreet while training only on Europe data are up to +2.8/1.2/1.6 on target data from Africa/Asia/Americas, and +4.6 overall on the hardest classes. Competitive performance is shown vs. few-shot target training, and analysis is provided to direct future study of geographical robustness.Comment: To appear in IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), 202

Prevalence of Dementia Among Older Patients: A Hospital-Based Study in Iran

Background: Dementia constitutes a public health hazard in developing countries. The aim of this study was to evaluate the prevalence of dementia and its associated factors in older hospitalized patients. Methods: The participants of this cross-sectional study consisted of older patients admitted to medical wards in Rasoul-e Akram hospital in Tehran, Iran. Mini-Mental State Examination, Mini-Cog test, Geriatric Depression Scale, Activities of Daily Living-Instrumental Activities of Daily Living (ADL-IADL) scale, and socioeconomic questionnaires were used. Results: A total of 205 elderly inpatients were included. The mean age was 71.33 ± 7.35 years; 63.4 of the participants had normal cognitive function, while 36.6 had some degree of cognitive impairment. There was a statistically significant relationship between gender, age, number of children, and occupation and the prevalence of dementia. Conclusion: Appropriate cognitive screening of older patients upon admission to hospitals could help identify potential adverse events and enhance the quality of care for patients with comorbid dementia. © The Author(s) 2019