Zihao Shao | Searcharxiv

arXiv2026

A Novel Scheme for Inter-Satellite Integrated Laser Ranging and Communication in Space-Based GW Detection

Adopting pseudo-random noise (PRN) codes for inter-satellite ranging, the resulting data-encoded PRN signal induces excessive laser phase measurement noise under the currently adopted integrated scheme in space-based gravitational wave (GW) detection systems. To suppress the noise, a scheme of inter-satellite integrated laser link is presented in this Letter, in which the data-encoded PRN signal for inter-satellite absolute distance measurement and communication is phase-modulated onto the ultra-stable oscillator (USO) clock signal before phase modulation onto the laser carrier. Theoretical analysis and simulation results show that in the proposed scheme the noise is significantly suppressed compared with the currently-adopted one, while the noise stemming from the data-encoded PRN signal on clock noise transfer is limited within the requirement.

gr-qc↗

arXiv2024

Vid-Morp: Video Moment Retrieval Pretraining from Unlabeled Videos in the Wild

Given a natural language query, video moment retrieval aims to localize the described temporal moment in an untrimmed video. A major challenge of this task is its heavy dependence on labor-intensive annotations for training. Unlike existing works that directly train models on manually curated data, we propose a novel paradigm to reduce annotation costs: pretraining the model on unlabeled, real-world videos. To support this, we introduce Video Moment Retrieval Pretraining (Vid-Morp), a large-scale dataset collected with minimal human intervention, consisting of over 50K videos captured in the wild and 200K pseudo annotations. Direct pretraining on these imperfect pseudo annotations, however, presents significant challenges, including mismatched sentence-video pairs and imprecise temporal boundaries. To address these issues, we propose the ReCorrect algorithm, which comprises two main phases: semantics-guided refinement and memory-consensus correction. The semantics-guided refinement enhances the pseudo labels by leveraging semantic similarity with video frames to clean out unpaired data and make initial adjustments to temporal boundaries. In the following memory-consensus correction phase, a memory bank tracks the model predictions, progressively correcting the temporal boundaries based on consensus within the memory. Comprehensive experiments demonstrate ReCorrect's strong generalization abilities across multiple downstream settings. Zero-shot ReCorrect achieves over 75% and 80% of the best fully-supervised performance on two benchmarks, while unsupervised ReCorrect reaches about 85% on both. The code, dataset, and pretrained models are available at https://github.com/baopj/Vid-Morp.

cs.CV↗