Egocentric video-language pretraining has significantly advanced video representation learning. Humans perceive and interact with a fully 3D world, developing spatial awareness that extends beyond text-based understanding. However, most previous works learn from 1D text or 2D visual cues, such as bounding boxes, which inherently lack 3D understanding. To bridge this gap, we introduce EgoDTM, an Egocentric Depth- and Text-aware Model, jointly trained through large-scale 3D-aware video pretraining and video-text contrastive learning. EgoDTM incorporates a lightweight 3D-aware decoder to efficiently learn 3D-awareness from pseudo depth maps generated by depth estimation models. To further facilitate 3D-aware video pretraining, we enrich the original brief captions with hand-object visual cues by organically combining several foundation models. Extensive experiments demonstrate EgoDTM's superior performance across diverse downstream tasks, highlighting its superior 3D-aware visual understanding. Code: \url{https://github.com/xuboshen/EgoDTM}.

With the surge in the amount of video data, video summarization techniques, including visual-modal(VM) and textual-modal(TM) summarization, are attracting more and more attention. However, unimodal summarization inevitably loses the rich semantics of the video. In this paper, we focus on a more comprehensive video summarization task named Bimodal Semantic Summarization of Videos(BiSSV). Specifically, we first construct a large-scale dataset, BIDS, in(video, VM-Summary, TM-Summary) triplet format. Unlike traditional processing methods, our construction procedure contains a VM-Summary extraction algorithm aiming to preserve the most salient content within long videos. Based on BIDS, we propose a Unified framework UBiSS for the BiSSV task, which models the saliency information in the video and generates a TM-summary and VM-summary simultaneously. We further optimize our model with a list-wise ranking-based objective to improve its capacity to capture highlights. Lastly, we propose a metric, NDCGMS, to provide a joint evaluation of the bimodal summary. Experiments show that our unified framework achieves better performance than multi-stage summarization pipelines. Code and data are available at https://github.com/MeiYutingg/UBiSS.

Due to the importance of protecting digital images in the modern world, digital image watermarking draws the attention of academia because of its applications in proving the authenticity and integrity of digital images. The performance of current watermarking algorithms needs to be improved to cope with the growing demand for better imperceptibility, greater robustness, and larger capacity. In response to the above requirements, a new robust blind watermarking algorithm is proposed in this paper, which mainly utilizes convolutional neural networks to embed watermarks in DCT domain. An attention module is introduced to calculate the confidence that each image pixel block contains watermark information, thus making our algorithm robust enough to deal with Crop attack. In addition, the introduction of a learning based solution for joint source and channel coding enables our algorithm to take grayscale images with simple backgrounds as input watermarks while maintaining great imperceptibility and robustness. Extensive experiments demonstrate the superiority of our algorithm.