Search Results for author:
Found 16 papers, 9 papers with code
Employing Multi-Estimations for Weakly-Supervised Semantic Segmentation
Instead of struggling to refine a single seed, we propose a novel approach to alleviate the inaccurate seed problem by leveraging the segmentation model's robustness to learn from multiple seeds.
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.
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.
Joint Power and 3D Trajectory Optimization for UAV-enabled Wireless Powered Communication Networks with Obstacles
For UTTOP, we first introduce a pretreatment method, and then use an improved particle swarm optimization with Normal distribution initialization, Genetic mechanism, Differential mechanism and Pursuit operator (PSO-NGDP) to deal with this sub optimization problem.
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.
DDG-Net: Discriminability-Driven Graph Network for Weakly-supervised Temporal Action Localization
Considering this phenomenon, we propose Discriminability-Driven Graph Network (DDG-Net), which explicitly models ambiguous snippets and discriminative snippets with well-designed connections, preventing the transmission of ambiguous information and enhancing the discriminability of snippet-level representations.
Weakly-supervised Temporal Action Localization
Weakly Supervised Temporal Action Localization
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.
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.
Pointly-Supervised Panoptic Segmentation
In this paper, we propose a new approach to applying point-level annotations for weakly-supervised panoptic segmentation.
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.
Towards Noiseless Object Contours for Weakly Supervised Semantic Segmentation
Existing methods usually generate pseudo labels from class activation map (CAM) and then train a segmentation model.
Learning Integral Objects With Intra-Class Discriminator for Weakly-Supervised Semantic Segmentation
Image-level weakly-supervised semantic segmentation (WSSS) aims at learning semantic segmentation by adopting only image class labels.
CIAN: Cross-Image Affinity Net for Weakly Supervised Semantic Segmentation
Weakly supervised semantic segmentation with only image-level labels saves large human effort to annotate pixel-level labels.
