When it comes to customising these algorithms for real-world applications, none of the existing libraries can offer both the flexibility of developing custom pose estimation algorithms and the high-performance of executing these algorithms on commodity devices.
In this work, we propose a no-reference video quality assessment method, aiming to achieve high-generalization capability in cross-content, -resolution and -frame rate quality prediction.
RLzoo provides developers with (i) high-level yet flexible APIs for prototyping DRL agents, and further customising the agents for best performance, (ii) a model zoo where users can import a wide range of DRL agents and easily compare their performance, and (iii) an algorithm that can automatically construct DRL agents with custom components (which are critical to improve agent's performance in custom applications).
To overcome those limitations, we propose a novel self-supervised model to synthesize garment images with disentangled attributes (e. g., collar and sleeves) without paired data.
This paper proposes a unique active relative localization mechanism for multi-agent Simultaneous Localization and Mapping(SLAM), in which a agent to be observed are considered as a task, which is performed by others assisting that agent by relative observation.
Additionally, we propose a DL-based joint signal detection and decoding for FTN signaling to replace the complete baseband part in traditional FTN receivers.