TextANIMAR: Text-based 3D Animal Fine-Grained Retrieval

3D object retrieval is an important yet challenging task, which has drawn more and more attention in recent years. While existing approaches have made strides in addressing this issue, they are often limited to restricted settings such as image and sketch queries, which are often unfriendly interactions for common users. In order to overcome these limitations, this paper presents a novel SHREC challenge track focusing on text-based fine-grained retrieval of 3D animal models. Unlike previous SHREC challenge tracks, the proposed task is considerably more challenging, requiring participants to develop innovative approaches to tackle the problem of text-based retrieval. Despite the increased difficulty, we believe that this task has the potential to drive useful applications in practice and facilitate more intuitive interactions with 3D objects. Five groups participated in our competition, submitting a total of 114 runs. While the results obtained in our competition are satisfactory, we note that the challenges presented by this task are far from being fully solved. As such, we provide insights into potential areas for future research and improvements. We believe that we can help push the boundaries of 3D object retrieval and facilitate more user-friendly interactions via vision-language technologies.Comment: arXiv admin note: text overlap with arXiv:2304.0573

Kangaroo Mother Care in Vietnam: A National Survey of a Middle-Income Country

Background: Kangaroo mother care (KMC) is a low-cost intervention that is indicated to be a highly effective practice for which adoption and implementation are lacking. We investigated the current provision of KMC in Vietnam and explored differences among levels of healthcare facility. Methods: A survey form was sent to 187 hospitals in Vietnam, representing the three levels (central, provincial and district) of public hospital-based maternity services. Results: Overall response rate was 74% (138/187 hospitals). Routine KMC implementation was estimated in 49% of the hospitals. Where KMC was implemented or was being introduced, half of the hospitals had a written protocol and a KMC-dedicated room, and held educational courses on KMC. KMC was mainly performed by the mother. Skin-to-skin contact was mostly performed for <12 h/day (55%), exclusive breastfeeding at discharge was very frequent (89%) and early discharge was considered in half of the hospitals (54%), while follow-up was not performed in 29% of the hospitals. Participants considered follow-up after discharge as the main barrier to KMC implementation, and indicated education (of both parents and health caregivers) and environment upgrades (KMC-dedicated room and equipment) as the most important facilitators. Conclusions: Our survey estimated a limited implementation of KMC in Vietnamese maternity hospitals, with marked variations across the different levels of maternity services. Areas of improvements include increasing the duration of skin-to-skin contact, arranging dedicated spaces for KMC, involving the relatives (especially at district level), extending the availability of a written protocol, improving the eligibility process, and implementing early discharge and follow-up monitoring

Damage Caused by <i>Bacchisa medioviolacea</i> Breuning (Coleoptera: Cerambycidae) in Wild Apple (<i>Docynia indica)</i> Orchards in Northwest Vietnam

The wood-borer Bacchisa medioviolacea Breuning (Coleoptera: Cerambycidae) is identified as a major new pest of Docynia indica (Rosales: Rosaceae) orchards in the northwest mountainous provinces of Vietnam. The life cycle extends over two years (721.7 days ± 17.6 days), with overwintering as larvae. Adults emerge and disperse in summer. Females lay 6–12 eggs during an oviposition period of 2–3 days, and the incubation period ranges from 27 to 38 days. The larval and pupal periods take 554–701 days and 40–59 days, respectively. Adults survive for 12–23 days. In 2019, the damage incidence (P%) and the damage index (DI) in Yen Bai, Lao Cai, Lai Chau, and Dien Bien provinces ranged from 43.5% to 71.6% and 0.80 to 1.78, respectively. Further research on the distribution and host range of B. medioviolacea is required to help formulate a management strategy for this new orchard pest

Damage Caused by Bacchisa medioviolacea Breuning (Coleoptera: Cerambycidae) in Wild Apple (Docynia indica) Orchards in Northwest Vietnam

The wood-borer Bacchisa medioviolacea Breuning (Coleoptera: Cerambycidae) is identified as a major new pest of Docynia indica (Rosales: Rosaceae) orchards in the northwest mountainous provinces of Vietnam. The life cycle extends over two years (721.7 days &plusmn; 17.6 days), with overwintering as larvae. Adults emerge and disperse in summer. Females lay 6&ndash;12 eggs during an oviposition period of 2&ndash;3 days, and the incubation period ranges from 27 to 38 days. The larval and pupal periods take 554&ndash;701 days and 40&ndash;59 days, respectively. Adults survive for 12&ndash;23 days. In 2019, the damage incidence (P%) and the damage index (DI) in Yen Bai, Lao Cai, Lai Chau, and Dien Bien provinces ranged from 43.5% to 71.6% and 0.80 to 1.78, respectively. Further research on the distribution and host range of B. medioviolacea is required to help formulate a management strategy for this new orchard pest

RGB-D to CAD retrieval with objectNN dataset

The goal of this track is to study and evaluate the performance of 3D object retrieval algorithms using RGB-D data. This is inspired from the practical need to pair an object acquired from a consumer-grade depth camera to CAD models available in public datasets on the Internet. To support the study, we propose ObjectNN, a new dataset with well segmented and annotated RGB-D objects from SceneNN [HPN*16] and CAD models from ShapeNet [CFG*15]. The evaluation results show that the RGB-D to CAD retrieval problem, while being challenging to solve due to partial and noisy 3D reconstruction, can be addressed to a good extent using deep learning techniques, particularly, convolutional neural networks trained by multi-view and 3D geometry. The best method in this track scores 82% in accuracy

SHREC\u2717: RgB-D to CAD Retrieval With ObjectNN Dataset

© 2017 The Eurographics Association. The goal of this track is to study and evaluate the performance of 3D object retrieval algorithms using RGB-D data. This is inspired from the practical need to pair an object acquired from a consumer-grade depth camera to CAD models available in public datasets on the Internet. To support the study, we propose ObjectNN, a new dataset with well segmented and annotated RGB-D objects from SceneNN [HPN∗16] and CAD models from ShapeNet [CFG∗15]. The evaluation results show that the RGB-D to CAD retrieval problem, while being challenging to solve due to partial and noisy 3D reconstruction, can be addressed to a good extent using deep learning techniques, particularly, convolutional neural networks trained by multi-view and 3D geometry. The best method in this track scores 82% in accuracy