193 research outputs found
Relationship marketing in service oriented companies: Retail pharmacy case study
Marketing is a moral responsibility of every modern company. All successful companies have to be marketing oriented. Marketing concept is based on the idea that customers and satisfaction of their needs and wants are always in focus of the companies, as most appropriate manner for increasing their profitability. Small companies are forced to be marketing oriented, also. Small companies have generally employed intuitions rather than marketing research techniques in their attempts to maximize customer satisfaction. Managers of small firms usually are focused on the fact that products and services they provide are those factors that will satisfy and retain the consumers. Most of small firm managers are not concerned with potential feedback from consumers. Relationship marketing as valuable tool for attracting and retaining consumers in small service oriented companies is defined in the study. How relationship marketing works in practice is shown through case study conducted in local retail pharmacy. Challenging situation for the pharmacy is to discover how to increase the profit when work with uniform prices of drugs. The research process conducted in the study has to provide insights into relationship marketing based on the patientβs behavior and patients buying decision making. Cognition of these two aspects would serve as good basis for developing further marketing actions for retaining the existing and attracting new patients as strategy for increasing the profit
Educational funcion of the school in contemporary society
Π variety of factors are involved in raising children but the most important are: family and school, i.e. teacher. Among other factors peers, social environment, mass - media (especially TV), NGOs, and political parties have very strong influence, and, with the development of information society, the impact of different social networks or the Internet is constantly increasing. So, we can state two types of influence: one that is spontaneous, unintentional, and the other - influence that is deliberate, intentional, with a specially emphasized purpose, tasks and means of implementation. In this context, the pedagogical thought distinguishes functional education (inadvertent, spontaneous impact) and guided, intentional impact which falls within the domain of intentional education. Intentional education is realized by the school or by the teacher, according to a specific program, with specific objectives, tasks, methods and means. Key words: school, educational function, contemporary society
Oral and maxillofacial rehabilitation of head and neck cancer patients
The general aim of this paper was to gain insight into the effects of maxillofacial rehabilitation in head and neck cancer patients using implantretained prostheses regarding treatment planning, implant survival, treatment outcome and quality of life. In R.Macedonia, this kinde of rehabilitation and research is very rare. This can finally contribute to improved rehabilitation of patients with head and neck cancer and to rise awerenes about the need of using implantretained prostheses regarding treatment planning, implant survival, treatment outcome and quality of life. Based on research done by Schoen (2004), we can concluded that, when following the technique described, a mandibulotomy can be combined safely with the insertion of implants in the ventral part of the edentulous mandible. We are about the first step in this area, hopefully that will develop proposed rehabilitation of head and neck cancer patients in Macedonia.
Key words:oral ; Maxillofacial; rehabilitation; Head and Neck Cancer; patients
ΠΠ΅Π½Π°ΡΠΈΡΠ°ΡΠ΅ Π½Π° ΠΎΠ΄Π½ΠΎΡΠΈΡΠ΅ ΡΠΎ ΠΊΠ»ΠΈΠ΅Π½ΡΠΈΡΠ΅ ΠΊΠ°ΠΊΠΎ ΠΎΡΠ½ΠΎΠ²Π° Π·Π° ΡΠ½Π°ΠΏΡΠ΅Π΄ΡΠ²Π°ΡΠ΅ Π½Π° ΠΊΠ²Π°Π»ΠΈΡΠ΅ΡΠΎΡ Π½Π° ΡΡΠ»ΡΠ³ΠΈΡΠ΅ Π½Π° ΡΠΌΠ΅ΡΠΊΠΎΠ²ΠΎΠ΄ΡΡΠ²Π΅Π½ΠΈΡΠ΅ ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΠΈ
ΠΠΎ Π΄Π΅Π½Π΅ΡΠ½ΠΎΡΠΎ Π±ΠΈΠ·Π½ΠΈΡ ΠΎΠΊΡΡΠΆΡΠ²Π°ΡΠ΅ ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΠΈΡΠ΅ ΡΓ¨ ΠΏΠΎΠ²Π΅ΡΠ΅ ΠΊΠΎΡΠΈΡΡΠ°Ρ Π½Π°Π΄Π²ΠΎΡΠ΅ΡΠ½ΠΈ Π΄Π°Π²Π°ΡΠ΅Π»ΠΈ Π½Π° ΡΡΠ»ΡΠ³ΠΈ Π·Π° ΠΎΠ΄ΡΠ΅Π΄Π΅Π½ΠΈ Π΄Π΅Π»ΠΎΠ²Π½ΠΈ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ, ΡΠΎ ΡΠ΅Π» Π΄Π° Π±ΠΈΠ΄Π°Ρ Π²ΠΎ ΠΌΠΎΠΆΠ½ΠΎΡΡ ΡΠ΅Π»ΠΎΡΠ½ΠΎ Π΄Π° ΡΠ΅ ΡΠΎΠΊΡΡΠΈΡΠ°Π°Ρ Π½Π° ΡΠ²ΠΎΡΠΎΡ ΠΎΡΠ½ΠΎΠ²Π΅Π½ Π±ΠΈΠ·Π½ΠΈΡ ΠΌΠΎΠ΄Π΅Π». ΠΠΎΠΌΠΏΠ°Π½ΠΈΠΈΡΠ΅ ΡΠΏΠΎΡΡΠ΅Π±ΡΠ²Π°Π°Ρ ΡΠΈΡΠΎΠΊΠ° Π»Π΅ΠΏΠ΅Π·Π° Π½Π° ΡΡΠ»ΡΠ³ΠΈ Π±ΠΈΠ·Π½ΠΈΡ ΠΊΠΎΠ½ Π±ΠΈΠ·Π½ΠΈΡ (Π2Π), ΡΠΎ ΠΈΠ½ΡΠ΅Π½Π·ΠΈΡΠ΅Ρ ΠΊΠΎΡ Π²Π°ΡΠΈΡΠ° Π²ΠΎ Π·Π°Π²ΠΈΡΠ½ΠΎΡΡ ΠΎΠ΄ ΠΏΡΠΈΡΠΎΠ΄Π°ΡΠ° Π½Π° ΠΈΠ½Π΄ΡΡΡΡΠΈΡΠ°ΡΠ°, ΠΏΡΠΈ ΡΡΠΎ Π½Π°ΡΡΠ΅ΡΡΠΎ ΡΠ΅ ΡΠ°Π±ΠΎΡΠΈ Π·Π° Π½Π°Π±Π°Π²ΠΊΠΈ Π²ΠΎ ΠΌΠ½ΠΎΠ³Ρ ΠΏΠΎΠ³ΠΎΠ»Π΅ΠΌ ΡΠ°Π·ΠΌΠ΅Ρ ΠΎΠ΄ ΠΎΠ½ΠΈΠ΅ ΡΡΠΎ Π³ΠΈ ΠΏΡΠ°Π²Π°Ρ ΠΏΠΎΠ΅Π΄ΠΈΠ½ΡΠΈ ΠΈΠ»ΠΈ ΡΠ΅ΠΌΠ΅ΡΡΡΠ²Π°. ΠΠ° ΠΆΠ°Π», ΠΏΠΎΡΡΠΎΡΡΠ²Π°ΡΠΈΡΠ΅ ΠΈΠ»ΠΈ Π±Π°ΡΠ°ΡΠ΅Π»ΠΈΡΠ΅ Π½Π° ΡΡΠ»ΡΠ³ΠΈ Π½Π΅ ΡΠ΅ ΡΠ΅ΠΊΠΎΠ³Π°Ρ Π·Π°Π΄ΠΎΠ²ΠΎΠ»Π½ΠΈ ΠΎΠ΄ ΠΊΠ²Π°Π»ΠΈΡΠ΅ΡΠΎΡ ΠΈ Π²ΡΠ΅Π΄Π½ΠΎΡΡΠ° Π½Π° ΡΡΠ»ΡΠ³ΠΈΡΠ΅ ΡΡΠΎ Π³ΠΈ Π΄ΠΎΠ±ΠΈΠ²Π°Π°Ρ. ΠΠΎΠ½Π΅ΠΊΠΎΠ³Π°Ρ ΠΈΡΠΊΡΡΡΠ²Π°ΡΠ° Π²ΠΎ Π²ΡΡΠΊΠ° ΡΠΎ Π½Π°Π±Π°Π²ΠΊΠ°ΡΠ° ΠΈ ΠΊΠΎΡΠΈΡΡΠ΅ΡΠ΅ΡΠΎ Π½Π° ΡΡΠ»ΡΠ³ΠΈΡΠ΅ ΡΠ΅ ΡΠΎΡΠ΅ΠΌΠ° ΠΊΠΎΡΠ΅ΠΊΡΠ½ΠΈ ΠΈ ΠΏΠΎΠ·ΠΈΡΠΈΠ²Π½ΠΈ, Π½ΠΎ ΠΏΠΎΡΡΠΎΡΠ°Ρ ΠΈ ΠΈΡΠΊΡΡΡΠ²Π° ΠΊΠΎΠΈ Π²ΠΎ Π³ΠΎΠ»Π΅ΠΌΠ° ΠΌΠ΅ΡΠ° ΡΠ΅ ΡΠ°Π·ΠΎΡΠ°ΡΡΠ²Π°ΡΠΊΠΈ, ΠΎΠ΄Π½ΠΎΡΠ½ΠΎ Π½Π΅ Π³ΠΈ ΠΈΡΠΏΠΎΠ»Π½ΡΠ²Π°Π°Ρ ΠΎΡΠ΅ΠΊΡΠ²Π°ΡΠ°ΡΠ°. Π Π°ΡΡΠ΅ΡΠΊΠ°ΡΠ° ΡΠ»ΠΎΠ³Π° Π½Π° ΡΡΠ»ΡΠ³ΠΈΡΠ΅, ΠΊΠ°ΠΊΠΎ Π³Π»ΠΎΠ±Π°Π»Π΅Π½ ΡΠ΅Π½ΠΎΠΌΠ΅Π½ ΠΊΠΎΡ ΡΠ° Π΄Π΅ΡΠ΅ΡΠΌΠΈΠ½ΠΈΡΠ° ΡΡΠΏΠ΅ΡΠ½ΠΎΡΡΠ° Π½Π° Π΅ΠΊΠΎΠ½ΠΎΠΌΠΈΠΈΡΠ΅ Π²ΠΎ ΡΠ²Π΅ΡΡΠΊΠΈ ΡΠ°ΠΌΠΊΠΈ, ΡΠ° ΠΏΠΎΡΠ΅Π½ΡΠΈΡΠ° ΠΏΠΎΡΡΠ΅Π±Π°ΡΠ° ΠΎΠ΄ ΠΎΡΠΎΡΠΌΡΠ²Π°ΡΠ΅ Π½Π° Π½Π°ΡΡΠ½Π° ΡΠ°ΠΌΠΊΠ° ΠΊΠΎΡΠ° Π·Π°ΡΠ΅Π±Π½ΠΎ ΡΠ΅ Π³ΠΈ ΡΡΠ΅ΡΠΈΡΠ° ΡΡΠ»ΡΠ³ΠΈΡΠ΅ ΠΊΠ°ΠΊΠΎ Π½Π°ΡΡΠ½ΠΎ ΠΏΠΎΠ΄ΡΠ°ΡΡΠ΅.
ΠΠ²Π°Π° Π΄ΠΎΠΊΡΠΎΡΡΠΊΠ° ΡΠ΅ΠΌΠ° ΡΠ΅ ΡΠΎΠΊΡΡΠΈΡΠ° Π½Π° Ρ.Π½. Π½Π°Π΄Π²ΠΎΡΠ΅ΡΠ½ΠΈ (outsourcing) Π΄Π°Π²Π°ΡΠ΅Π»ΠΈ Π½Π° ΡΠΌΠ΅ΡΠΊΠΎΠ²ΠΎΠ΄ΡΡΠ²Π΅Π½ΠΈ ΡΡΠ»ΡΠ³ΠΈ. ΠΡΠ½ΠΎΠ²Π½Π°ΡΠ° ΡΠ΅Π», ΡΠΈΠ½ΡΠ΅ΡΠΈΠ·ΠΈΡΠ°ΡΡΠΈ Π½Π°ΡΠ½ΠΎΠ²ΠΈ Π½Π°ΡΡΠ½ΠΈ ΠΈΡΡΡΠ°ΠΆΡΠ²Π°ΡΠ° ΠΎΠ΄ ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½Π°ΡΠ° ΠΎΠ±Π»Π°ΡΡ, Π΅ Π΄Π° ΡΠ΅ ΠΏΡΠΎΡΡΠΈ ΠΈ ΡΡΡΡΠΊΡΡΠΈΡΠ° Π΄Π΅Π»ΠΎΠ²Π½ΠΈΠΎΡ ΠΎΠ΄Π½ΠΎΡ Π½Π° ΡΠ΅Π»Π°ΡΠΈΡΠ° Π΄Π°Π²Π°ΡΠ΅Π»-ΠΊΠΎΡΠΈΡΠ½ΠΈΠΊ Π½Π° ΡΠΌΠ΅ΡΠΊΠΎΠ²ΠΎΠ΄ΡΡΠ²Π΅Π½ΠΈ ΡΡΠ»ΡΠ³ΠΈ, Π΄Π° ΡΠ΅ ΠΏΡΠ΅Π·Π΅Π½ΡΠΈΡΠ°Π°Ρ ΠΊΠ°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠΈΡΠ΅ Π½Π° ΡΡΠ»ΡΠΆΠ½ΠΈΠΎΡ ΠΏΡΠΎΠ΄ΡΠΊΡ ΠΈ ΡΡΠ»ΡΠΆΠ½Π°ΡΠ° ΠΎΠΊΠΎΠ»ΠΈΠ½Π° ΠΈ Π΄Π° ΡΠ΅ ΠΏΠΎΠ½ΡΠ΄ΠΈ Π±ΠΈΠ·Π½ΠΈΡ ΠΌΠΎΠ΄Π΅Π» ΠΊΠΎΡ ΡΠ΅ ΠΎΠ²ΠΎΠ·ΠΌΠΎΠΆΠΈ Π΄ΠΎΠ΄Π°Π΄Π΅Π½Π° Π²ΡΠ΅Π΄Π½ΠΎΡΡ Π½Π° ΡΡΠ»ΡΠ³Π°ΡΠ° ΡΡΠΎ ΡΠ΅ Π½ΡΠ΄ΠΈ. ΠΠΎΠ΄ ΠΏΡΠ΅ΡΠΏΠΎΡΡΠ°Π²ΠΊΠ° Π΄Π΅ΠΊΠ° Π΄Π°Π²Π°ΡΠ΅Π»ΠΈΡΠ΅ Π½Π° ΡΠΌΠ΅ΡΠΊΠΎΠ²ΠΎΠ΄ΡΡΠ²Π΅Π½ΠΈ ΡΡΠ»ΡΠ³ΠΈ ΠΏΠΎΡΠ΅Π΄ΡΠ²Π°Π°Ρ ΡΠΈΠ»Π½ΠΎ ΠΈΡΡΠ°ΠΊΠ½Π°ΡΠ° Π΅ΠΊΡΠΏΠ΅ΡΡΠΈΠ·Π° ΠΎΠ΄ ΠΎΠ±Π»Π°ΡΡΠ° Π½Π° ΡΠΌΠ΅ΡΠΊΠΎΠ²ΠΎΠ΄ΡΡΠ²Π΅Π½ΠΈΡΠ΅ Π½Π°ΡΠΊΠΈ, ΠΎΠ²Π°Π° ΡΠ΅ΠΌΠ° ΡΠ΅ ΠΏΡΠΈΠ΄ΠΎΠ½Π΅ΡΠ΅ ΠΈΡΡΠΈΡΠ΅ Π΄Π° Π³ΠΎ ΠΊΡΠ΅Π½Π°Ρ ΡΠ²ΠΎΠ΅ΡΠΎ ΠΊΡΠΈΡΠΈΡΠΊΠΎ ΡΠ°Π·ΠΌΠΈΡΠ»ΡΠ²Π°ΡΠ΅ Π½Π° Π΅Π΄Π½ΠΎ ΠΏΠΎΠ²ΠΈΡΠΎΠΊΠΎ Π½ΠΈΠ²ΠΎ, Π²ΠΌΠ΅ΡΠ½ΡΠ²Π°ΡΡΠΈ ΠΌΠ°ΡΠΊΠ΅ΡΠΈΠ½Π³ - ΠΌΠ΅Π½Π°ΡΠΌΠ΅Π½Ρ ΠΏΡΠΈΡΡΠ°ΠΏ Π²ΠΎ ΡΠ²ΠΎΠ΅ΡΠΎ ΡΠ°Π±ΠΎΡΠ΅ΡΠ΅. Π£ΠΏΡΠ°Π²ΡΠ²Π°ΡΠ΅ΡΠΎ Π½Π° Π΄Π΅Π»ΠΎΠ²Π½ΠΈΡΠ΅ ΠΎΠ΄Π½ΠΎΡΠΈ ΡΠΎ ΠΊΠ»ΠΈΠ΅Π½ΡΠΈΡΠ΅ Π΅ ΠΏΡΠΎΡΠ΅Ρ ΠΊΠΎΡ ΡΡΠ΅Π±Π° Π΄Π° ΡΠ΅ ΠΌΠ΅Π½Π°ΡΠΈΡΠ°, ΡΠΏΡΠ°Π²ΡΠ²Π° ΠΈ ΠΈΠ½Π²Π΅ΡΡΠΈΡΠ°.
ΠΡΠ°Π²ΠΈΠ»Π½ΠΎΡΠΎ ΡΡΡΡΠΊΡΡΠΈΡΠ°ΡΠ΅ Π½Π° Π΄Π΅Π»ΠΎΠ²Π½ΠΈΠΎΡ ΠΎΠ΄Π½ΠΎΡ ΠΎΠ²ΠΎΠ·ΠΌΠΎΠΆΡΠ²Π° ΠΏΠΎΠ²Π΅ΡΠ΅ΠΊΡΠ°ΡΠ½ΠΈ ΠΏΡΠΈΠ΄ΠΎΠ±ΠΈΠ²ΠΊΠΈ Π·Π° ΡΡΡΠ°Π½ΠΈΡΠ΅ ΡΠΈΠ½ΠΈΡΠ΅Π»ΠΈ Π½Π° ΠΈΡΡΠΈΠΎΡ. ΠΡΠ΅ΠΊΡ ΡΠ²Π΅ΡΠ½ΠΎ ΡΠΏΡΠ°Π²ΡΠ²Π°ΡΠ΅ Π½Π° ΠΎΠ΄Π½ΠΎΡΠΈΡΠ΅ ΡΠΎ ΠΊΠ»ΠΈΠ΅Π½ΡΠΈΡΠ΅ ΠΎΠ΄ ΡΡΡΠ°Π½Π° Π½Π° Π΄Π°Π²Π°ΡΠ΅Π»ΠΈΡΠ΅ Π½Π° ΡΠΌΠ΅ΡΠΊΠΎΠ²ΠΎΠ΄ΡΡΠ²Π΅Π½ΠΈ ΡΡΠ»ΡΠ³ΠΈ, ΡΠ΅ ΠΎΠ²ΠΎΠ·ΠΌΠΎΠΆΡΠ²Π° ΡΠΏΠΎΠ·Π½Π°Π²Π°ΡΠ΅ Π½Π° ΡΠΏΠ΅ΡΠΈΡΠΈΡΠ½ΠΈΡΠ΅ ΠΏΠΎΠ±Π°ΡΡΠ²Π°ΡΠ° Π½Π° ΠΊΠ»ΠΈΠ΅Π½ΡΠΈΡΠ΅-ΠΊΠΎΡΠΈΡΠ½ΠΈΡΠΈ. ΠΠ° ΡΠΎΠΎΠ΄Π²Π΅ΡΠ½ΠΎ Π΄Π° ΡΠ΅ ΠΏΠΎΠ·ΠΈΡΠΈΠΎΠ½ΠΈΡΠ°Π°Ρ Π½Π° ΠΏΠ°Π·Π°ΡΠΎΡ, ΡΠΌΠ΅ΡΠΊΠΎΠ²ΠΎΠ΄ΠΈΡΠ΅Π»ΠΈΡΠ΅ ΡΠ΅ ΠΈΡΠΏΡΠ°Π²Π΅Π½ΠΈ ΠΏΡΠ΅Π΄ ΠΏΡΠ΅Π΄ΠΈΠ·Π²ΠΈΠΊΠΎΡ ΠΊΠΎΠ½ΡΠΈΠ½ΡΠΈΡΠ°Π½ΠΎ Π΄Π° Π³ΠΎ Π½Π°Π΄Π³ΡΠ°Π΄ΡΠ²Π°Π°Ρ ΡΠΎΠΏΡΡΠ²Π΅Π½ΠΎΡΠΎ Π·Π½Π°Π΅ΡΠ΅ ΠΈ ΡΠΌΠ΅Π΅ΡΠ΅ ΠΈ ΠΏΠΎΡΡΠΎΡΠ°Π½ΠΎ Π΄Π° ΠΏΡΠΎΠ½Π°ΠΎΡΠ°Π°Ρ Π½Π°ΡΠΈΠ½ΠΈ, ΠΌΠ΅ΡΠΎΠ΄ΠΈ ΠΈ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ, ΡΠΎ ΠΊΠΎΠΈ Π±ΠΈ ΡΠ° ΡΠ½Π°ΠΏΡΠ΅Π΄ΠΈΠ»Π΅ ΡΡΠ»ΡΠ³Π°ΡΠ° ΠΊΠΎΡΠ° ΡΠ° Π½ΡΠ΄Π°Ρ ΠΊΠΎΠ½ ΠΊΠ»ΠΈΠ΅Π½ΡΠΎΡ - ΠΊΠΎΡΠΈΡΠ½ΠΈΠΊ. ΠΠ°ΠΊΠΎ ΡΠ΅ΠΎΠΏΡΠ°ΡΠ½Π° Π±ΠΈΠ·Π½ΠΈΡ ΡΠΈΠ»ΠΎΠ·ΠΎΡΠΈΡΠ° ΠΊΠΎΡΠ° ΠΎΠ²ΠΎΠ·ΠΌΠΎΠΆΡΠ²Π° ΡΠ½Π°ΠΏΡΠ΅Π΄ΡΠ²Π°ΡΠ΅ Π½Π° ΠΎΠ΄Π½ΠΎΡΠΎΡ Π΄Π°Π²Π°ΡΠ΅Π» - ΠΊΠΎΡΠΈΡΠ½ΠΈΠΊ Π½Π° ΡΠΌΠ΅ΡΠΊΠΎΠ²ΠΎΠ΄ΡΡΠ²Π΅Π½ΠΈ ΡΡΠ»ΡΠ³ΠΈ, Π²ΠΎ Π΄ΠΎΠΊΡΠΎΡΡΠΊΠΈΠΎΡ ΡΡΡΠ΄ Π΅ ΠΏΡΠ΅Π·Π΅Π½ΡΠΈΡΠ°Π½ ΠΊΠΎΠ½ΡΠ΅ΠΏΡΠΎΡ Π½Π°ΡΠ΅ΡΠ΅Π½ CRM (Customer Relationship Management) ΠΈΠ»ΠΈ ΠΠ΅Π½Π°ΡΠΈΡΠ°ΡΠ΅ Π½Π° ΠΎΠ΄Π½ΠΎΡΠΈΡΠ΅ ΡΠΎ ΠΊΠ»ΠΈΠ΅Π½ΡΠΈΡΠΈΡΠ΅. Π¦Π Π ΡΠΈΡΡΠ΅ΠΌΠΈΡΠ΅ ΠΏΡΠ΅ΡΡΡΠ°Π²ΡΠ²Π°Π°Ρ ΡΠΈΠ½ΡΠ΅Π·Π° Π½Π° ΡΠΎΡΡΠ²Π΅Ρ ΠΈ Π±ΠΈΠ·Π½ΠΈΡ ΡΠΈΠ»ΠΎΠ·ΠΎΡΠΈΡΠ°, ΠΊΠΎΠΈ Π²ΠΎ ΡΠΎΠΊΡΡ Π³ΠΈ ΠΈΠΌΠ°Π°Ρ ΡΠΏΠ΅ΡΠΈΡΠΈΡΠ½ΠΈΡΠ΅ ΠΏΠΎΠ±Π°ΡΡΠ²Π°ΡΠ° Π½Π° ΠΊΠ»ΠΈΠ΅Π½ΡΠΈΡΠ΅. ΠΡΠ΅ΠΏΠΎΠ·Π½Π°Π²Π°ΡΠ΅ΡΠΎ Π½Π° ΠΏΡΠΈΠ΄ΠΎΠ±ΠΈΠ²ΠΊΠΈΡΠ΅ ΠΊΠΎΠΈ Π³ΠΈ Π½ΡΠ΄Π°Ρ ΠΎΠ²ΠΈΠ΅ ΡΠΈΡΡΠ΅ΠΌΠΈ ΠΎΠ²ΠΎΠ·ΠΌΠΎΠΆΡΠ²Π° ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΠΈΡΠ΅ Π΄Π°Π²Π°ΡΠ΅Π»ΠΈ Π½Π° ΡΠΌΠ΅ΡΠΊΠΎΠ²ΠΎΠ΄ΡΡΠ²Π΅Π½ΠΈ ΡΡΠ»ΡΠ³ΠΈ Π΄Π° ΡΠ΅ ΡΠ½Π°Π±Π΄Π°Ρ ΡΠΎ ΡΠ΅Π»Π΅Π²Π°Π½ΡΠ½ΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ Π·Π° ΠΊΠ°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠΈΡΠ΅ Π½Π° ΡΠΎΠΏΡΡΠ²Π΅Π½ΠΈΡΠ΅ ΠΊΠ»ΠΈΠ΅Π½ΡΠΈ ΠΈ Π΄Π° ΠΏΠΎΠ½ΡΠ΄Π°Ρ Π΄ΠΎΠ΄Π°Π΄Π΅Π½Π° Π²ΡΠ΅Π΄Π½ΠΎΡΡ Π½Π° ΡΡΠ»ΡΠ³ΠΈΡΠ΅ ΠΊΠΎΠΈ Π³ΠΈ Π½ΡΠ΄Π°Ρ, ΡΡΠΎ ΡΠ΅ Π²Π»ΠΈΡΠ°Π΅ Π½Π° Π·Π³ΠΎΠ»Π΅ΠΌΡΠ²Π°ΡΠ΅ Π½Π° Π»ΠΎΡΠ°Π»Π½ΠΎΡΡΠ° Π½Π° ΠΏΠΎΡΡΠΎΠ΅ΡΠΊΠΈΡΠ΅ ΠΊΠ»ΠΈΠ΅Π½ΡΠΈ, ΡΠ΅ ΠΎΠ²ΠΎΠ·ΠΌΠΎΠΆΠΈ ΠΏΠΎΠ΄Π»ΠΎΠ³Π° Π·Π° ΡΠ°Π·Π²ΠΈΠ²Π°ΡΠ΅ Π½Π° ΡΡΡΠ°ΡΠ΅Π³ΠΈΡΠ° Π·Π° ΠΏΡΠΈΠ²Π»Π΅ΠΊΡΠ²Π°ΡΠ΅ Π½Π° Π½ΠΎΠ²ΠΈ ΠΊΠ»ΠΈΠ΅Π½ΡΠΈ ΠΈ Π²ΠΎ ΠΊΡΠ°ΡΠ½Π° Π»ΠΈΠ½ΠΈΡΠ° ΡΠ΅ ΡΠ΅Π·ΡΠ»ΡΠΈΡΠ° Π²ΠΎ Π·Π³ΠΎΠ»Π΅ΠΌΡΠ²Π°ΡΠ΅ Π½Π° ΠΏΡΠΎΡΠΈΡΠ°Π±ΠΈΠ»Π½ΠΎΡΡΠ° Π½Π° ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΠΈΡΠ΅ Π΄Π°Π²Π°ΡΠ΅Π»ΠΈ Π½Π° ΡΠΌΠ΅ΡΠΊΠΎΠ²ΠΎΠ΄ΡΡΠ²Π΅Π½ΠΈ ΡΡΠ»ΡΠ³ΠΈ.
ΠΠ»ΡΡΠ½ΠΈ Π·Π±ΠΎΡΠΎΠ²ΠΈ: ΡΠΌΠ΅ΡΠΊΠΎΠ²ΠΎΠ΄ΡΡΠ²ΠΎ, ΡΡΠ»ΡΠ³ΠΈ, ΠΡΡΡΠΎΡΡΠΈΠ½Π³, ΠΎΠ΄Π½Π΅ΡΡΠ²Π°ΡΠ΅, ΠΏΠΎΡΡΠΎΡΡΠ²Π°ΡΠΈ, ΠΌΠ°ΡΠΊΠ΅ΡΠΈΠ½Π³, ΠΌΠ΅Π½Π°ΡΠΈΡΠ°Ρ
In-Domain Self-Supervised Learning Can Lead to Improvements in Remote Sensing Image Classification
Self-supervised learning (SSL) has emerged as a promising approach for remote
sensing image classification due to its ability to leverage large amounts of
unlabeled data. In contrast to traditional supervised learning, SSL aims to
learn representations of data without the need for explicit labels. This is
achieved by formulating auxiliary tasks that can be used to create
pseudo-labels for the unlabeled data and learn pre-trained models. The
pre-trained models can then be fine-tuned on downstream tasks such as remote
sensing image scene classification. The paper analyzes the effectiveness of SSL
pre-training using Million AID - a large unlabeled remote sensing dataset on
various remote sensing image scene classification datasets as downstream tasks.
More specifically, we evaluate the effectiveness of SSL pre-training using the
iBOT framework coupled with Vision transformers (ViT) in contrast to supervised
pre-training of ViT using the ImageNet dataset. The comprehensive experimental
work across 14 datasets with diverse properties reveals that in-domain SSL
leads to improved predictive performance of models compared to the supervised
counterparts
- β¦