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The Object Relations Lens
Some psychoanalytic models focus on \"how\" and \"when\" particular events may have shaped an individual's emotional and behavioral trajectories in life. In a field as accelerated as psychiatry, it's tempting to use this information to rush to a diagnosis. The object relations model, as clearly outlined in this compelling volume from Dr. Christopher Miller, offers an attractive alternative: it emphasizes how a patient's early development has informed interpersonal relationship templates and how these play out in the here-and-now of the clinical encounter. As accessible to the trainee as it is relevant to the experienced clinician, this guide describes how leaning into the therapist-patient dyad (including transference-countertransference dynamics) provides a fertile ground for learning about the patient's past more vividly. Among the book's standout features are: • Clinical vignettes that richly illustrate object relations theory as applied within therapy sessions as well as in acute care settings• Experience-near guidance on assimilating the concepts in academic settings, best practices for utilizing supervision, and extensive literature recommendations• Discussions of other theoretical approaches (e.g., attachment theory), as well as a dedicated chapter on a neuroscientific model of object relations, demonstrating how this psychodynamic framework can be harmonized within psychiatric theory and practice• A chapter focused on termination, including advice for inviting the patient into the decision-making process With its mix of theory, practical advice, and illustrative clinical material, The Object Relations Lens is an indispensable resource for any clinician hoping to gain further knowledge of object relations thought and how this perspective can be eminently useful when conceptualizing and working with patients.
eBook
Understanding the Role of Objects in Cross-Disciplinary Collaboration
In this paper we make a case for the use of multiple theoretical perspectives—theory on boundary objects, epistemic objects, cultural historical activity theory, and objects as infrastructure—to understand the role of objects in cross-disciplinary collaboration. A pluralist approach highlights that objects perform at least three types of work in this context: they motivate collaboration, they allow participants to work across different types of boundaries, and they constitute the fundamental infrastructure of the activity. Building on the results of an empirical study, we illustrate the insights that each theoretical lens affords into practices of collaboration and develop a novel analytical framework that organizes objects according to the active work they perform. Our framework can help shed new light on the phenomenon, especially with regard to the shifting status of objects and sources of conflict (and change) in collaboration. After discussing these novel insights, we outline directions for future research stemming from a pluralist approach. We conclude by noting the managerial implications of our findings.
Journal Article
The Unmanned Aerial Vehicle Benchmark: Object Detection, Tracking and Baseline
With the increasing popularity of Unmanned Aerial Vehicles (UAVs) in computer vision-related applications, intelligent UAV video analysis has recently attracted the attention of an increasing number of researchers. To facilitate research in the UAV field, this paper presents a UAV dataset with 100 videos featuring approximately 2700 vehicles recorded under unconstrained conditions and 840k manually annotated bounding boxes. These UAV videos were recorded in complex real-world scenarios and pose significant new challenges, such as complex scenes, high density, small objects, and large camera motion, to the existing object detection and tracking methods. These challenges have encouraged us to define a benchmark for three fundamental computer vision tasks, namely, object detection, single object tracking (SOT) and multiple object tracking (MOT), on our UAV dataset. Specifically, our UAV benchmark facilitates evaluation and detailed analysis of state-of-the-art detection and tracking methods on the proposed UAV dataset. Furthermore, we propose a novel approach based on the so-called Context-aware Multi-task Siamese Network (CMSN) model that explores new cues in UAV videos by judging the consistency degree between objects and contexts and that can be used for SOT and MOT. The experimental results demonstrate that our model could make tracking results more robust in both SOT and MOT, showing that the current tracking and detection methods have limitations in dealing with the proposed UAV benchmark and that further research is indeed needed.
Journal Article
Improved Object Detection Method Utilizing YOLOv7-Tiny for Unmanned Aerial Vehicle Photographic Imagery
In unmanned aerial vehicle photographs, object detection algorithms encounter challenges in enhancing both speed and accuracy for objects of different sizes, primarily due to complex backgrounds and small objects. This study introduces the PDWT-YOLO algorithm, based on the YOLOv7-tiny model, to improve the effectiveness of object detection across all sizes. The proposed method enhances the detection of small objects by incorporating a dedicated small-object detection layer, while reducing the conflict between classification and regression tasks through the replacement of the YOLOv7-tiny model’s detection head (IDetect) with a decoupled head. Moreover, network convergence is accelerated, and regression accuracy is improved by replacing the Complete Intersection over Union (CIoU) loss function with a Wise Intersection over Union (WIoU) focusing mechanism in the loss function. To assess the proposed model’s effectiveness, it was trained and tested on the VisDrone-2019 dataset comprising images captured by various drones across diverse scenarios, weather conditions, and lighting conditions. The experiments show that mAP@0.5:0.95 and mAP@0.5 increased by 5% and 6.7%, respectively, with acceptable running speed compared with the original YOLOv7-tiny model. Furthermore, this method shows improvement over other datasets, confirming that PDWT-YOLO is effective for multiscale object detection.
Journal Article
Love, Fear, and Health
Using attachment theory, Maunder and Hunter provide a practical,
clinically focused introduction to the influence of attachment
styles on an individual's risk of disease and the effectiveness of
their interactions with health care providers.
eBook