arXiv subjects

Shihao Su

Publications and source records attributed to Shihao Su.

3 recordsLinked to original sources

arXiv2026

Pion Transition Form Factor in Lattice QCD

We investigate the neutral pion transition form factor $F_{\pi^0\gamma^\ast\gamma^\ast}(q_1^2,q_2^2)$ in lattice QCD and confirm that the connected and disconnected contributions have the same sign. We employ the recently proposed blending method, which supplies an unbiased and cheap estimators for the required all-to-all propagators. The external pion states are treated within the distillation framework, while the electromagnetic currents are evaluated in the full blending space. Numerical tests are performed on an $N_f=2+1$ lattice ensemble. Our result shows that the contribution of the disconnected part is approximately $1\%$ of that of the connected part and enables constructive interference of probability amplitudes.

hep-lat↗

arXiv2023

PUPS: Point Cloud Unified Panoptic Segmentation

Point cloud panoptic segmentation is a challenging task that seeks a holistic solution for both semantic and instance segmentation to predict groupings of coherent points. Previous approaches treat semantic and instance segmentation as surrogate tasks, and they either use clustering methods or bounding boxes to gather instance groupings with costly computation and hand-crafted designs in the instance segmentation task. In this paper, we propose a simple but effective point cloud unified panoptic segmentation (PUPS) framework, which use a set of point-level classifiers to directly predict semantic and instance groupings in an end-to-end manner. To realize PUPS, we introduce bipartite matching to our training pipeline so that our classifiers are able to exclusively predict groupings of instances, getting rid of hand-crafted designs, e.g. anchors and Non-Maximum Suppression (NMS). In order to achieve better grouping results, we utilize a transformer decoder to iteratively refine the point classifiers and develop a context-aware CutMix augmentation to overcome the class imbalance problem. As a result, PUPS achieves 1st place on the leader board of SemanticKITTI panoptic segmentation task and state-of-the-art results on nuScenes.

cs.CV↗

arXiv2021

When Video Classification Meets Incremental Classes

With the rapid development of social media, tremendous videos with new classes are generated daily, which raise an urgent demand for video classification methods that can continuously update new classes while maintaining the knowledge of old videos with limited storage and computing resources. In this paper, we summarize this task as Class-Incremental Video Classification (CIVC) and propose a novel framework to address it. As a subarea of incremental learning tasks, the challenge of catastrophic forgetting is unavoidable in CIVC. To better alleviate it, we utilize some characteristics of videos. First, we decompose the spatio-temporal knowledge before distillation rather than treating it as a whole in the knowledge transfer process; trajectory is also used to refine the decomposition. Second, we propose a dual granularity exemplar selection method to select and store representative video instances of old classes and key-frames inside videos under a tight storage budget. We benchmark our method and previous SOTA class-incremental learning methods on Something-Something V2 and Kinetics datasets, and our method outperforms previous methods significantly.

cs.CV↗