Impact of pseudo depth on open world object segmentation with minimal user guidance

Pseudo depth maps are depth map predicitions which are used as ground truth during training. In this paper we leverage pseudo depth maps in order to segment objects of classes that have never been seen during training. This renders our object segmentation task an open world task. The pseudo depth maps are generated using pretrained networks, which have either been trained with the full intention to generalize to downstream tasks (LeRes and MiDaS), or which have been trained in an unsupervised fashion on video sequences (MonodepthV2). In order to tell our network which object to segment, we provide the network with a single click on the object's surface on the pseudo depth map of the image as input. We test our approach on two different scenarios: One without the RGB image and one where the RGB image is part of the input. Our results demonstrate a considerably better generalization performance from seen to unseen object types when depth is used. On the Semantic Boundaries Dataset we achieve an improvement from 61.57 to 69.79 IoU score on unseen classes, when only using half of the training classes during training and performing the segmentation on depth maps only

All keypoints you need: detecting arbitrary keypoints on the body of triple, high, and long jump athletes

Performance analyses based on videos are commonly used by coaches of athletes in various sports disciplines. In individual sports, these analyses mainly comprise the body posture. This paper focuses on the disciplines of triple, high, and long jump, which require fine-grained locations of the athlete’s body. Typical human pose estimation datasets provide only a very limited set of keypoints, which is not sufficient in this case. Therefore, we propose a method to detect arbitrary keypoints on the whole body of the athlete by leveraging the limited set of annotated keypoints and auto- generated segmentation masks of body parts. Evaluations show that our model is capable of detecting keypoints on the head, torso, hands, feet, arms, and legs, including also bent elbows and knees. We analyze and compare different techniques to encode desired keypoints as the model’s input and their embedding for the Transformer backbone

Segformer++: efficient token-merging strategies for high-resolution semantic segmentation

Utilizing transformer architectures for semantic segmentation of high-resolution images is hindered by the attention’s quadratic computational complexity in the number of tokens. A solution to this challenge involves decreasing the number of tokens through token merging, which has exhibited remarkable enhancements in inference speed, training efficiency, and memory utilization for image classification tasks. In this paper, we explore various token merging strategies within the framework of the Segformer architecture and perform experiments on multiple semantic segmentation and human pose estimation datasets. Notably, without model re-training, we, for example, achieve an inference acceleration of 61% on the Cityscapes dataset while maintaining the mIoU performance. Consequently, this paper facilitates the deployment of transformer-based architectures on resource-constrained devices and in real-time applications

Institutions, Consensus and Conflict: Implications for Policy and Practice

Summary This article reflects on the challenges faced when the ideal of consensual communities is questioned. A more complex view of institutional relationships at the local level is envisaged, one which emphasises conflict as much as consensus. This, in turn, suggests some implications for institutional design and processes of conflict negotiation. A number of alternatives are explored, ranging from targeted, institutional design to more flexible, learning process approaches. Support for effective negotiation processes is highlighted, including the enhancement of claims?making capacity through processes of participation and empowerment. Due to the inherent uncertainties in both ecological and social dynamics, institutional design can never take a blueprint form. Instead, a flexible, adaptive style of dealing with institutional complexity and uncertainty is envisaged. Despite the necessity of disagreggating ‘community’ imagery for local?level implementation, such imagery can also be used strategically and effectively by local people and other development actors in struggles to define and direct processes of change. ancement of claims?making capacity through processes of participation and empowerment. Due to the inherent uncertainties in both ecological and social dynamics, institutional design can never take a blueprint form. Instead, a flexible, adaptive style of dealing with institutional complexity and uncertainty is envisaged. Despite the necessity of disagreggating ‘community’ imagery for local?level implementation, such imagery can also be used strategically and effectively by local people and other development actors in struggles to define and direct processes of change

Dissipation in circuit quantum electrodynamics: lasing and cooling of a low-frequency oscillator

Superconducting qubits coupled to electric or nanomechanical resonators display effects previously studied in quantum electrodynamics (QED) and extensions thereof. Here we study a driven qubit coupled to a low-frequency tank circuit with particular emphasis on the role of dissipation. When the qubit is driven to perform Rabi oscillations, with Rabi frequency in resonance with the oscillator, the latter can be driven far from equilibrium. Blue detuned driving leads to a population inversion in the qubit and lasing behavior of the oscillator ("single-atom laser"). For red detuning the qubit cools the oscillator. This behavior persists at the symmetry point where the qubit-oscillator coupling is quadratic and decoherence effects are minimized. Here the system realizes a "single-atom-two-photon laser".Comment: 9 pages, written for the Focus Issue of New J. Phys. on "Mechanical Systems at the Quantum Limit", ed. by Markus Aspelmeyer and Keith Schwa