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Found 9 papers, 6 papers with code
Segment Anything for Videos: A Systematic Survey
To address this gap, this work conducts a systematic review on SAM for videos in the era of foundation models.
A Comprehensive Survey on Human Video Generation: Challenges, Methods, and Insights
The goal of this survey is to offer the research community a clear and holistic view of the advancements in human video generation, highlighting the milestones achieved and the challenges that lie ahead.
WebUOT-1M: Advancing Deep Underwater Object Tracking with A Million-Scale Benchmark
Most existing trackers are tailored for open-air environments, leading to performance degradation when applied to UOT due to domain gaps.
Recent Advances in Predictive Modeling with Electronic Health Records
This survey systematically reviews recent advances in deep learning-based predictive models using EHR data.
A Comprehensive Survey on Segment Anything Model for Vision and Beyond
As the first to comprehensively review the progress of segmenting anything task for vision and beyond based on the foundation model of SAM, this work focuses on its applications to various tasks and data types by discussing its historical development, recent progress, and profound impact on broad applications.
WebUAV-3M: A Benchmark for Unveiling the Power of Million-Scale Deep UAV Tracking
In this work, we propose WebUAV-3M, the largest public UAV tracking benchmark to date, to facilitate both the development and evaluation of deep UAV trackers.
Unified Style Transfer
Currently, it is hard to compare and evaluate different style transfer algorithms due to chaotic definitions of style and the absence of agreed objective validation methods in the study of style transfer.
ConCAD: Contrastive Learning-based Cross Attention for Sleep Apnea Detection
With recent advancements in deep learning methods, automatically learning deep features from the original data is becoming an effective and widespread approach.
Hippocampus-heuristic Character Recognition Network for Zero-shot Learning
To this end, this paper proposes a novel Hippocampus-heuristic Character Recognition Network (HCRN), which references the way of hippocampus thinking, and can recognize unseen Chinese characters (namely zero-shot learning) only by training part of radicals.
