Cross-Domain Point Cloud Recognition with Deep Learning

Point cloud recognition using deep learning methods has attracted increasing research interest recently due to its great potential in real-world applications such as autonomous driving, robotics, etc. However, point clouds of similar objects often exhibit notable geometric variations due to the difference in capturing devices or environmental changes. This leads to significant performance degradation when a learnt point cloud recognition model is applied to a new scenario, which is also known as the domain adaptation issue. In this thesis, we first provide a comprehensive literature review of deep learning on visual recognition, unsupervised domain adaptation, open-set unsupervised domain adaptation, self-supervised learning and knowledge transfer to introduce the background of the thesis. Then, an introduction to the problem setting and the commonly used benchmark datasets is provided for a better understanding of the task. Next, a point-level domain adaptive point sampling (DAPS) strategy is proposed to tackle the domain gap in cross-domain point cloud recognition. In addition, an instance-level domain adaptive cloud sampling (DACS) strategy is proposed to learn additional target-specific information for better recognition performance on the target domain. Moreover, we further propose a two-stage open-set domain adaptive sampling (OS-DAS) strategy to learn an open-set recognition model in a coarse-to-fine manner to tackle the open-set unsupervised domain adaptation issue. Finally, we list some potential research directions for cross-domain point cloud recognition

Binary Latent Diffusion

In this paper, we show that a binary latent space can be explored for compact yet expressive image representations. We model the bi-directional mappings between an image and the corresponding latent binary representation by training an auto-encoder with a Bernoulli encoding distribution. On the one hand, the binary latent space provides a compact discrete image representation of which the distribution can be modeled more efficiently than pixels or continuous latent representations. On the other hand, we now represent each image patch as a binary vector instead of an index of a learned cookbook as in discrete image representations with vector quantization. In this way, we obtain binary latent representations that allow for better image quality and high-resolution image representations without any multi-stage hierarchy in the latent space. In this binary latent space, images can now be generated effectively using a binary latent diffusion model tailored specifically for modeling the prior over the binary image representations. We present both conditional and unconditional image generation experiments with multiple datasets, and show that the proposed method performs comparably to state-of-the-art methods while dramatically improving the sampling efficiency to as few as 16 steps without using any test-time acceleration. The proposed framework can also be seamlessly scaled to $1024 \times 1024$ high-resolution image generation without resorting to latent hierarchy or multi-stage refinements

Estudio de correlación entre citoquinas proinflamatorias en lágrima y humor acuoso con el edema macular cistoide postcirugía de catarata

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Medicina, leída el 30-06-2022Las cataratas siguen siendo una de las principales causas de ceguera en todo el mundo. La cirugía de catarata es el tratamiento curativo de elección. El edema macular cistoidepseudoafáquico (EMCP) es la causa más común de la pérdida de visión posoperatoria, que se manifiesta por acumulación de líquido en el área macular a las 4 a 6 semanas después de la cirugía. Se trata de una complicación autolimitada cuya fisiopatología sigue siendo, en parte, desconocida. Actualmente, la prueba multiplex basada en microesferas permite analizar simultáneamente variascitoquinas/quimioquinas, el uso de esta tecnología podría contribuir a mejorar la comprensión delpapel de estas moléculas en la fisiopatología de varias enfermedades. Hasta donde sabemos, no existe estudios previos sobre la correlación entre los niveles basales de citoquinas/quimioquinas en la lágrima y el humor acuoso antes de la cirugía y la aparición de EMCP...Cataracts remain one of the leading causes of blindness worldwide. Cataract surgery is the curative treatment of choice. Pseudophaquic cystoid macular edema (PCME) is the most common cause of vision loss after cataract surgery and manifests as an accumulation of fluid in the macular area 4 to 6 weeks after surgery. PCME is a self-limited complication whose physiopathology remains partially unknown. Currently, bead-based multiplex assay enables the simultaneous analysis of several cytokines/chemokines and the use of this technology could contribute to a better understanding of the role of such molecules in the physiopathology of several diseases. As far as we know there are no previous studies on the correlation between basal levels on cytokines/chemokines in tears and aqueous humor before cataract surgery and the development of PCME...Fac. de MedicinaTRUEunpu