In 3D data analysis, point clouds provide detailed geometric insights for applications like computer vision and geospatial analysis. However, their irregularity and diversity make classification challenging, especially in domain generalization, where models must generalize to new data distributions. Our research introduces a novel 3D Domain Generalization (3DDG) method using Unsupervised Part Decomposition (UPD) and Graph Structure Induction (GSI). The UPD module employs spectral clustering and a modified Shannon entropy method to segment point clouds into meaningful parts. The GSI module constructs a graph of these parts' spatial relationships, processed by a Graph Neural Network (GNN) to understand complex geometries. Our approach enhances part-based analysis, improving classification accuracy on the PointDA-10 and GraspNetPC-10 datasets by 1.25% and 2.6%, respectively. These results highlight our advancements in 3D domain generalization, enabling more robust classification models for diverse point cloud data.

The popularity of egocentric cameras and their always-on nature has lead to the abundance of day long first-person videos. The highly redundant nature of these videos and extreme camera-shakes make them difficult to watch from beginning to end. These videos require efficient summarization tools for consumption. However, traditional summarization techniques developed for static surveillance videos or highly curated sports videos and movies are either not suitable or simply do not scale for such hours long videos in the wild. On the other hand, specialized summarization techniques developed for egocentric videos limit their focus to important objects and people. This paper presents a novel unsupervised reinforcement learning framework to summarize egocentric videos both in terms of length and the content. The proposed framework facilitates incorporating various prior preferences such as faces, places, or scene diversity and interactive user choice in terms of including or excluding the particular type of content. This approach can also be adapted to generate summaries of various lengths, making it possible to view even 1-minute summaries of one's entire day. When using the facial saliency-based reward, we show that our approach generates summaries focusing on social interactions, similar to the current state-of-the-art (SOTA). The quantitative comparisons on the benchmark Disney dataset show that our method achieves significant improvement in Relaxed F-Score (RFS) (29.60 compared to 19.21 from SOTA), BLEU score (0.68 compared to 0.67 from SOTA), Average Human Ranking (AHR), and unique events covered. Finally, we show that our technique can be applied to summarize traditional, short, hand-held videos as well, where we improve the SOTA F-score on benchmark SumMe and TVSum datasets from 41.4 to 46.40 and 57.6 to 58.3 respectively. We also provide a Pytorch implementation and a web demo at https://pravin74.github.io/Int-sum/index.html.

The popularity of egocentric cameras and their always-on nature has lead to the abundance of day-long first-person videos. Because of the extreme shake and highly redundant nature, these videos are difficult to watch from beginning to end and often require summarization tools for their efficient consumption. However, traditional summarization techniques developed for static surveillance videos, or highly curated sports videos and movies are, either, not suitable or simply do not scale for such hours long videos in the wild. On the other hand, specialized summarization techniques developed for egocentric videos limit their focus to important objects and people. In this paper, we present a novel unsupervised reinforcement learning technique to generate video summaries from day long egocentric videos. Our approach can be adapted to generate summaries of various lengths making it possible to view even 1-minute summaries of one's entire day. The technique can also be adapted to various rewards, such as distinctiveness and indicativeness of the summary. When using the facial saliency-based reward, we show that our approach generates summaries focusing on social interactions, similar to the current state-of-the-art (SOTA). Quantitative comparison on the benchmark Disney dataset shows that our method achieves significant improvement in Relaxed F-Score (RFS) (32.56 vs. 19.21) and BLEU score (12.12 vs. 10.64). Finally, we show that our technique can be applied for summarizing traditional, short, hand-held videos as well, where we improve the SOTA F-score on benchmark SumMe and TVSum datasets from 41.4 to 45.6 and 57.6 to 59.1 respectively.