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Found 12 papers, 1 papers with code
Neural Radiance Fields for Transparent Object Using Visual Hull
Unlike opaque object, novel view synthesis of transparent object is a challenging task, because transparent object refracts light of background causing visual distortions on the transparent object surface along the viewpoint change.
Single Image Reflection Removal with Reflection Intensity Prior Knowledge
In this paper, we propose a general reflection intensity prior that captures the intensity of the reflection phenomenon and demonstrate its effectiveness.
Internal-External Boundary Attention Fusion for Glass Surface Segmentation
Inspired by prior semantic segmentation approaches with challenging image types such as X-ray or CT scans, we propose separated internal-external boundary attention modules that individually learn and selectively integrate visual characteristics of the inside and outside region of glass surface from a single color image.
Faster Segment Anything: Towards Lightweight SAM for Mobile Applications
Concretely, we distill the knowledge from the heavy image encoder (ViT-H in the original SAM) to a lightweight image encoder, which can be automatically compatible with the mask decoder in the original SAM.
Segment Anything Model (SAM) Meets Glass: Mirror and Transparent Objects Cannot Be Easily Detected
Meta AI Research has recently released SAM (Segment Anything Model) which is trained on a large segmentation dataset of over 1 billion masks.
Continual Learning with Neuron Activation Importance
Continual learning is a concept of online learning with multiple sequential tasks.
Unrealistic Feature Suppression for Generative Adversarial Networks
However, sampling approaches which discard samples show limitations in some aspects such as the speed of training and optimality of the networks.
Spatially Decomposed Hinge Adversarial Loss by Local Gradient Amplifier
Generative Adversarial Networks (GANs) have achieved large attention and great success in various research areas, but it still suffers from training instability.
Continual learning with neural activation importance
Continual learning is a concept of online learning along with multiple sequential tasks.
Sub-clusters of Normal Data for Anomaly Detection
We hypothesize that if there exists reasonably good feature space semantically separating sub-clusters of given normal data, unseen anomaly also can be well distinguished in the space from the normal data.
Mode Penalty Generative Adversarial Network with adapted Auto-encoder
To this end, generator network of GAN learns implicit distribution of real data set from the classification with candidate generated samples.
Small Noisy and Perspective Face Detection using Deformable Symmetric Gabor Wavelet Network
Face detection and tracking in low resolution image is not a trivial task due to the limitation in the appearance features for face characterization.
