Tokencut: Segmenting objects in images and videos with self-supervised transformer and normalized cut
In this paper, we describe a graph-based algorithm that uses the features obtained by a self-
supervised transformer to detect and segment salient objects in images and videos. With this …
supervised transformer to detect and segment salient objects in images and videos. With this …
A survey on masked autoencoder for self-supervised learning in vision and beyond
Masked autoencoders are scalable vision learners, as the title of MAE\cite {he2022masked},
which suggests that self-supervised learning (SSL) in vision might undertake a similar …
which suggests that self-supervised learning (SSL) in vision might undertake a similar …
Auxiliary tasks benefit 3d skeleton-based human motion prediction
Exploring spatial-temporal dependencies from observed motions is one of the core
challenges of human motion prediction. Previous methods mainly focus on dedicated …
challenges of human motion prediction. Previous methods mainly focus on dedicated …
Ponder: Point cloud pre-training via neural rendering
We propose a novel approach to self-supervised learning of point cloud representations by
differentiable neural rendering. Motivated by the fact that informative point cloud features …
differentiable neural rendering. Motivated by the fact that informative point cloud features …
Firerisk: A remote sensing dataset for fire risk assessment with benchmarks using supervised and self-supervised learning
S Shen, S Seneviratne, X Wanyan… - … Conference on Digital …, 2023 - ieeexplore.ieee.org
In recent decades, wildfires have caused tremendous property losses, fatalities, and
extensive damage to forest ecosystems. Inspired by the abundance of publicly available …
extensive damage to forest ecosystems. Inspired by the abundance of publicly available …
Disentangling spatial and temporal learning for efficient image-to-video transfer learning
Recently, large-scale pre-trained language-image models like CLIP have shown
extraordinary capabilities for understanding spatial contents, but naively transferring such …
extraordinary capabilities for understanding spatial contents, but naively transferring such …
Traj-mae: Masked autoencoders for trajectory prediction
Trajectory prediction has been a crucial task in building a reliable autonomous driving
system by anticipating possible dangers. One key issue is to generate consistent trajectory …
system by anticipating possible dangers. One key issue is to generate consistent trajectory …
Rethinking tokenizer and decoder in masked graph modeling for molecules
Masked graph modeling excels in the self-supervised representation learning of molecular
graphs. Scrutinizing previous studies, we can reveal a common scheme consisting of three …
graphs. Scrutinizing previous studies, we can reveal a common scheme consisting of three …
Multi-robot scene completion: Towards task-agnostic collaborative perception
Collaborative perception learns how to share information among multiple robots to perceive
the environment better than individually done. Past research on this has been task-specific …
the environment better than individually done. Past research on this has been task-specific …
Switchtab: Switched autoencoders are effective tabular learners
Self-supervised representation learning methods have achieved significant success in
computer vision and natural language processing (NLP), where data samples exhibit explicit …
computer vision and natural language processing (NLP), where data samples exhibit explicit …