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Found 10 papers, 3 papers with code
Diffusion-driven GAN Inversion for Multi-Modal Face Image Generation
To do this, we combine the strengths of Generative Adversarial networks (GANs) and diffusion models (DMs) by employing the multi-modal features in the DM into the latent space of the pre-trained GANs.
Sparse multi-view hand-object reconstruction for unseen environments
In contrast, sparse multi-view methods can take advantage of the additional views to tackle occlusion, while keeping the computational cost low compared to dense multi-view methods.
Open-vocabulary object 6D pose estimation
We introduce the new setting of open-vocabulary object 6D pose estimation, in which a textual prompt is used to specify the object of interest.
Boosting Video Object Segmentation based on Scale Inconsistency
We present a refinement framework to boost the performance of pre-trained semi-supervised video object segmentation (VOS) models.
Learning by Erasing: Conditional Entropy based Transferable Out-Of-Distribution Detection
For a new in-distribution (ID) dataset, existing methods require retraining to capture the dataset-specific feature representation or data distribution.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Improving Generalization of Deep Networks for Estimating Physical Properties of Containers and Fillings
We present methods to estimate the physical properties of household containers and their fillings manipulated by humans.
A Wavelet-based Dual-stream Network for Underwater Image Enhancement
The multi-color space fusion network takes the decomposed structure image as input and estimates the color corrected output by employing the feature representations from diverse color spaces of the input.
Wide and Narrow: Video Prediction from Context and Motion
Video prediction, forecasting the future frames from a sequence of input frames, is a challenging task since the view changes are influenced by various factors, such as the global context surrounding the scene and local motion dynamics.
Semantically Adversarial Learnable Filters
The proposed framework combines a structure loss and a semantic adversarial loss in a multi-task objective function to train a fully convolutional neural network.
EdgeFool: An Adversarial Image Enhancement Filter
This loss function accounts for both image detail enhancement and class misleading objectives.
