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Found 20 papers, 7 papers with code
MOWA: Multiple-in-One Image Warping Model
To our knowledge, this is the first work that solves multiple practical warping tasks in one single model.
Viewpoint-Aware Visual Grounding in 3D Scenes
In this paper we investigate the significance of viewpoint information in 3D visual grounding -- introducing a model that explicitly predicts the speaker's viewpoint based on the referring expression and scene.
Towards Robust and Expressive Whole-body Human Pose and Shape Estimation
Whole-body pose and shape estimation aims to jointly predict different behaviors (e. g., pose, hand gesture, facial expression) of the entire human body from a monocular image.
SARA: Controllable Makeup Transfer with Spatial Alignment and Region-Adaptive Normalization
To address this problem, we propose a novel Spatial Alignment and Region-Adaptive normalization method (SARA) in this paper.
DI-Net : Decomposed Implicit Garment Transfer Network for Digital Clothed 3D Human
3D virtual try-on enjoys many potential applications and hence has attracted wide attention.
LCReg: Long-Tailed Image Classification with Latent Categories based Recognition
In this work, we tackle the challenging problem of long-tailed image recognition.
CoactSeg: Learning from Heterogeneous Data for New Multiple Sclerosis Lesion Segmentation
Moreover, a simple and effective relation regularization is proposed to ensure the longitudinal relations among the three outputs to improve the model learning.
Harmonizing Base and Novel Classes: A Class-Contrastive Approach for Generalized Few-Shot Segmentation
Current methods for few-shot segmentation (FSSeg) have mainly focused on improving the performance of novel classes while neglecting the performance of base classes.
Modeling Continuous Motion for 3D Point Cloud Object Tracking
The task of 3D single object tracking (SOT) with LiDAR point clouds is crucial for various applications, such as autonomous driving and robotics.
Reliability-Adaptive Consistency Regularization for Weakly-Supervised Point Cloud Segmentation
Weakly-supervised point cloud segmentation with extremely limited labels is highly desirable to alleviate the expensive costs of collecting densely annotated 3D points.
Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation
Weakly supervised point cloud segmentation, i. e. semantically segmenting a point cloud with only a few labeled points in the whole 3D scene, is highly desirable due to the heavy burden of collecting abundant dense annotations for the model training.
Long-tailed Recognition by Learning from Latent Categories
Previous long-tailed recognition methods commonly focus on the data augmentation or re-balancing strategy of the tail classes to give more attention to tail classes during the model training.
Ranked #9 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation
The pixel-level smoothness forces the model to generate invariant results under adversarial perturbations.
MV-TON: Memory-based Video Virtual Try-on network
With the development of Generative Adversarial Network, image-based virtual try-on methods have made great progress.
Few-Shot Segmentation with Global and Local Contrastive Learning
To this end, we first propose a prior extractor to learn the query information from the unlabeled images with our proposed global-local contrastive learning.
Learning Meta-class Memory for Few-Shot Semantic Segmentation
To explicitly learn meta-class representations in few-shot segmentation task, we propose a novel Meta-class Memory based few-shot segmentation method (MM-Net), where we introduce a set of learnable memory embeddings to memorize the meta-class information during the base class training and transfer to novel classes during the inference stage.
Remember What You have drawn: Semantic Image Manipulation with Memory
Therefore, in this paper, we propose a memory-based Image Manipulation Network (MIM-Net), where a set of memories learned from images is introduced to synthesize the texture information with the guidance of the textual description.
Exploring Bottom-up and Top-down Cues with Attentive Learning for Webly Supervised Object Detection
To reduce the human labeling effort, we propose a novel webly supervised object detection (WebSOD) method for novel classes which only requires the web images without further annotations.
M2E-Try On Net: Fashion from Model to Everyone
Instead, we present a novel virtual Try-On network, M2E-Try On Net, which transfers the clothes from a model image to a person image without the need of any clean product images.
Keypoint Based Weakly Supervised Human Parsing
We develop an iterative learning method to generate pseudo part segmentation masks from keypoint labels.
