Search Results for author:
Found 16 papers, 12 papers with code
SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization
In real-world scenarios, achieving domain generalization (DG) presents significant challenges as models are required to generalize to unknown target distributions.
FGFusion: Fine-Grained Lidar-Camera Fusion for 3D Object Detection
In this paper, we propose Fine-Grained Lidar-Camera Fusion (FGFusion) that make full use of multi-scale features of image and point cloud and fuse them in a fine-grained way.
Multi-scale Edge-based U-shape Network for Salient Object Detection
Deep-learning based salient object detection methods achieve great improvements.
A lightweight multi-scale context network for salient object detection in optical remote sensing images
Meanwhile, in order to accurately detect complete salient objects in complex backgrounds, we design an attention-based pyramid feature aggregation mechanism for gradually aggregating and refining the salient regions from the multi-scale context extraction module.
MVP Matching: A Maximum-Value Perfect Matching for Mining Hard Samples, With Application to Person Re-Identification
It leads to significant improvements in both convergence rate and recognition performance.
Polycentric Clustering and Structural Regularization for Source-free Unsupervised Domain Adaptation
In this paper, a novel framework named PCSR is proposed to tackle SFDA via a novel intra-class Polycentric Clustering and Structural Regularization strategy.
MPI: Multi-receptive and Parallel Integration for Salient Object Detection
The semantic representation of deep features is essential for image context understanding, and effective fusion of features with different semantic representations can significantly improve the model's performance on salient object detection.
Attention Guided Network for Salient Object Detection in Optical Remote Sensing Images
Specifically, the position enhancement stage consists of a semantic attention module and a contextual attention module to accurately describe the approximate location of salient objects.
A Task-aware Dual Similarity Network for Fine-grained Few-shot Learning
The goal of fine-grained few-shot learning is to recognize sub-categories under the same super-category by learning few labeled samples.
MonoSIM: Simulating Learning Behaviors of Heterogeneous Point Cloud Object Detectors for Monocular 3D Object Detection
The insight behind introducing MonoSIM is that we propose to simulate the feature learning behaviors of a point cloud based detector for monocular detector during the training period.
Robust Ensembling Network for Unsupervised Domain Adaptation
In this paper, we propose a Robust Ensembling Network (REN) for UDA, which applies a robust time ensembling teacher network to learn global information for domain transfer.
SIDE: Self-supervised Intermediate Domain Exploration for Source-free Domain Adaptation
Domain adaptation aims to alleviate the domain shift when transferring the knowledge learned from the source domain to the target domain.
Learning with Batch-wise Optimal Transport Loss for 3D Shape Recognition
It can accelerate the convergence rate significantly while achieving a state-of-the-art recognition performance.
Real-Time Elderly Monitoring for Senior Safety by Lightweight Human Action Recognition
Real-time monitoring and action recognition are essential to raise an alert timely when abnormal behaviors or unusual activities occur.
SF-FSDA: Source-Free Few-Shot Domain Adaptive Object Detection with Efficient Labeled Data Factory
Domain adaptive object detection aims to leverage the knowledge learned from a labeled source domain to improve the performance on an unlabeled target domain.
Prompting Diffusion Representations for Cross-Domain Semantic Segmentation
Intrigued by this result, we set out to explore how well diffusion-pretrained representations generalize to new domains, a crucial ability for any representation.
