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Found 10 papers, 5 papers with code
Learning Topology Uniformed Face Mesh by Volume Rendering for Multi-view Reconstruction
Our goal is to leverage the superiority of neural volume rendering into multi-view reconstruction of face mesh with consistent topology.
SDPose: Tokenized Pose Estimation via Circulation-Guide Self-Distillation
To mitigate the problem of under-fitting, we design a transformer module named Multi-Cycled Transformer(MCT) based on multiple-cycled forwards to more fully exploit the potential of small model parameters.
Make-It-Vivid: Dressing Your Animatable Biped Cartoon Characters from Text
Creating and animating 3D biped cartoon characters is crucial and valuable in various applications.
Repositioning the Subject within Image
Our research reveals that the fundamental sub-tasks of subject repositioning, which include filling the void left by the repositioned subject, reconstructing obscured portions of the subject and blending the subject to be consistent with surrounding areas, can be effectively reformulated as a unified, prompt-guided inpainting task.
Rethinking Amodal Video Segmentation from Learning Supervised Signals with Object-centric Representation
Furthermore, we propose a multi-view fusion layer based temporal module which is equipped with a set of object slots and interacts with features from different views by attention mechanism to fulfill sufficient object representation completion.
Unsupervised Open-Vocabulary Object Localization in Videos
In this paper, we show that recent advances in video representation learning and pre-trained vision-language models allow for substantial improvements in self-supervised video object localization.
RFENet: Towards Reciprocal Feature Evolution for Glass Segmentation
Glass-like objects are widespread in daily life but remain intractable to be segmented for most existing methods.
A Simple Test-Time Method for Out-of-Distribution Detection
In contrast, this paper proposes a simple Test-time Linear Training (ETLT) method for OOD detection.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Clustering by the Probability Distributions from Extreme Value Theory
In contrast, the GPD fits the distribution of distance to the centroid exceeding a sufficiently large threshold, leading to a more stable performance of GPD k-means.
An Improved Composite Functional Gradient Learning by Wasserstein Regularization for Generative adversarial networks
However, the discriminator using logistic regression from the CFG framework is gradually hard to discriminate between real and fake images while the training steps go on.
