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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
Love, Fear, and Health
Can the way in which we relate to others seriously affect our health? Can understanding those attachments help health care providers treat us better? In Love, Fear, and Health , psychiatrists Robert Maunder and Jonathan Hunter draw on evidence from neuroscience, stress physiology, social psychology, and evolutionary biology to explain how understanding attachment – the ways in which people seek security in their close relationships – can transform patient outcomes.
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. Drawing on more than fifty years of combined experience as health care providers, teachers, and researchers, they explain in clear language how health care workers in all disciplines can use this knowledge to meet their patients’ needs better and to improve their health.
eBook
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
DC-YOLOv8: Small-Size Object Detection Algorithm Based on Camera Sensor
Traditional camera sensors rely on human eyes for observation. However, human eyes are prone to fatigue when observing objects of different sizes for a long time in complex scenes, and human cognition is limited, which often leads to judgment errors and greatly reduces efficiency. Object recognition technology is an important technology used to judge the object’s category on a camera sensor. In order to solve this problem, a small-size object detection algorithm for special scenarios was proposed in this paper. The advantage of this algorithm is that it not only has higher precision for small-size object detection but also can ensure that the detection accuracy for each size is not lower than that of the existing algorithm. There are three main innovations in this paper, as follows: (1) A new downsampling method which could better preserve the context feature information is proposed. (2) The feature fusion network is improved to effectively combine shallow information and deep information. (3) A new network structure is proposed to effectively improve the detection accuracy of the model. From the point of view of detection accuracy, it is better than YOLOX, YOLOR, YOLOv3, scaled YOLOv5, YOLOv7-Tiny, and YOLOv8. Three authoritative public datasets are used in these experiments: (a) In the Visdron dataset (small-size objects), the map, precision, and recall ratios of DC-YOLOv8 are 2.5%, 1.9%, and 2.1% higher than those of YOLOv8s, respectively. (b) On the Tinyperson dataset (minimal-size objects), the map, precision, and recall ratios of DC-YOLOv8 are 1%, 0.2%, and 1.2% higher than those of YOLOv8s, respectively. (c) On the PASCAL VOC2007 dataset (normal-size objects), the map, precision, and recall ratios of DC-YOLOv8 are 0.5%, 0.3%, and 0.4% higher than those of YOLOv8s, respectively.
Journal Article