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Found 10 papers, 5 papers with code
SMURF: Continuous Dynamics for Motion-Deblurring Radiance Fields
Neural radiance fields (NeRF) has attracted considerable attention for their exceptional ability in synthesizing novel views with high fidelity.
Synchronizing Vision and Language: Bidirectional Token-Masking AutoEncoder for Referring Image Segmentation
While these methods match language features with image features to effectively identify likely target objects, they often struggle to correctly understand contextual information in complex and ambiguous sentences and scenes.
Conditional Generative Representation for Black-Box Optimization with Implicit Constraints
This approach leverages a conditional variational autoencoder to learn the distribution of feasible decisions, enabling a two-way mapping between the original decision space and a simplified, constraint-free latent space.
Mesh Represented Recycle Learning for 3D Hand Pose and Mesh Estimation
To this end, we propose a mesh represented recycle learning strategy for 3D hand pose and mesh estimation which reinforces synthesized hand mesh representation in a training phase.
Treating Motion as Option with Output Selection for Unsupervised Video Object Segmentation
Unsupervised video object segmentation (VOS) is a task that aims to detect the most salient object in a video without external guidance about the object.
Hybrid model for Single-Stage Multi-Person Pose Estimation
In general, human pose estimation methods are categorized into two approaches according to their architectures: regression (i. e., heatmap-free) and heatmap-based methods.
Guided Slot Attention for Unsupervised Video Object Segmentation
Unsupervised video object segmentation aims to segment the most prominent object in a video sequence.
Leveraging Spatio-Temporal Dependency for Skeleton-Based Action Recognition
In this paper, we propose the Spatio-Temporal Curve Network (STC-Net) to effectively leverage the spatio-temporal dependency of the human skeleton.
Boundary-aware Camouflaged Object Detection via Deformable Point Sampling
The proposed DPS-Net utilizes a Deformable Point Sampling transformer (DPS transformer) that can effectively capture sparse local boundary information of significant object boundaries in COD using a deformable point sampling method.
Hierarchically Decomposed Graph Convolutional Networks for Skeleton-Based Action Recognition
Graph convolutional networks (GCNs) are the most commonly used methods for skeleton-based action recognition and have achieved remarkable performance.
Ranked #4 on
Skeleton Based Action Recognition
on NTU RGB+D 120
