Search Results for author:
Found 22 papers, 7 papers with code
MaterialSeg3D: Segmenting Dense Materials from 2D Priors for 3D Assets
Driven by powerful image diffusion models, recent research has achieved the automatic creation of 3D objects from textual or visual guidance.
SceneX:Procedural Controllable Large-scale Scene Generation via Large-language Models
Due to its great application potential, large-scale scene generation has drawn extensive attention in academia and industry.
Continual Forgetting for Pre-trained Vision Models
(i) For unwanted knowledge, efficient and effective deleting is crucial.
Segment Anything in 3D Gaussians
In this paper, we propose a novel approach to achieve object segmentation in 3D Gaussian via an interactive procedure without any training process and learned parameters.
FurniScene: A Large-scale 3D Room Dataset with Intricate Furnishing Scenes
Indoor scene generation has attracted significant attention recently as it is crucial for applications of gaming, virtual reality, and interior design.
Bootstrap Masked Visual Modeling via Hard Patches Mining
To empower the model as a teacher, we propose Hard Patches Mining (HPM), predicting patch-wise losses and subsequently determining where to mask.
CompenHR: Efficient Full Compensation for High-resolution Projector
Full projector compensation is a practical task of projector-camera systems.
Informative Data Mining for One-Shot Cross-Domain Semantic Segmentation
One-shot domain adaptation methods attempt to overcome these challenges by transferring the pre-trained source model to the target domain using only one target data.
DropPos: Pre-Training Vision Transformers by Reconstructing Dropped Positions
As it is empirically observed that Vision Transformers (ViTs) are quite insensitive to the order of input tokens, the need for an appropriate self-supervised pretext task that enhances the location awareness of ViTs is becoming evident.
TDG: Text-guided Domain Generalization
Domain generalization (DG) attempts to generalize a model trained on single or multiple source domains to the unseen target domain.
DiffusePast: Diffusion-based Generative Replay for Class Incremental Semantic Segmentation
The Class Incremental Semantic Segmentation (CISS) extends the traditional segmentation task by incrementally learning newly added classes.
Using Unreliable Pseudo-Labels for Label-Efficient Semantic Segmentation
A common practice is to select the highly confident predictions as the pseudo-ground-truths for each pixel, but it leads to a problem that most pixels may be left unused due to their unreliability.
Pulling Target to Source: A New Perspective on Domain Adaptive Semantic Segmentation
To this end, we propose T2S-DA, which we interpret as a form of pulling Target to Source for Domain Adaptation, encouraging the model in learning similar cross-domain features.
Hard Patches Mining for Masked Image Modeling
We observe that the reconstruction loss can naturally be the metric of the difficulty of the pre-training task.
A Survey of Deep Visual Cross-Domain Few-Shot Learning
Research into Cross-Domain Few-Shot (CDFS) has emerged to address this issue, forming a more challenging and realistic setting.
SSF: Accelerating Training of Spiking Neural Networks with Stabilized Spiking Flow
Surrogate gradient (SG) is one of the most effective approaches for training spiking neural networks (SNNs).
TexPrax: A Messaging Application for Ethical, Real-time Data Collection and Annotation
We evaluate TexPrax in a user-study with German factory employees who ask their colleagues for solutions on problems that arise during their daily work.
Self-Supervised Predictive Learning: A Negative-Free Method for Sound Source Localization in Visual Scenes
Sound source localization in visual scenes aims to localize objects emitting the sound in a given image.
Remember the Difference: Cross-Domain Few-Shot Semantic Segmentation via Meta-Memory Transfer
To remedy this problem, we propose an interesting and challenging cross-domain few-shot semantic segmentation task, where the training and test tasks perform on different domains.
Source Data-Free Cross-Domain Semantic Segmentation: Align, Teach and Propagate
It is the first work to use negative pseudo labels during self-training for domain adaptation.
Uncertainty-Aware Pseudo Label Refinery for Domain Adaptive Semantic Segmentation
Unsupervised domain adaptation for semantic segmentation aims to assign the pixel-level labels for unlabeled target domain by transferring knowledge from the labeled source domain.
High-speed real-time single-pixel microscopy based on Fourier sampling
However, according to CS theory, image reconstruction is an iterative process that consumes enormous amounts of computational time and cannot be performed in real time.
