Search Results for author:
Found 11 papers, 4 papers with code
Progressive Random Convolutions for Single Domain Generalization
Single domain generalization aims to train a generalizable model with only one source domain to perform well on arbitrary unseen target domains.
Improving Test-Time Adaptation via Shift-agnostic Weight Regularization and Nearest Source Prototypes
This paper proposes a novel test-time adaptation strategy that adjusts the model pre-trained on the source domain using only unlabeled online data from the target domain to alleviate the performance degradation due to the distribution shift between the source and target domains.
Learning to Discriminate Information for Online Action Detection: Analysis and Application
To overcome this problem, we propose a novel recurrent unit, named Information Discrimination Unit (IDU), which explicitly discriminates the information relevancy between an ongoing action and others to decide whether to accumulate the input information.
Few-shot Open-set Recognition by Transformation Consistency
Based on the transformation consistency, our method measures the difference between the transformed prototypes and a modified prototype set.
Just a Few Points Are All You Need for Multi-View Stereo: A Novel Semi-Supervised Learning Method for Multi-View Stereo
We generate the spare ground truth of the DTU dataset for evaluation and extensive experiments verify that our SGT-MVSNet outperforms the state-of-the-art MVS methods on the sparse ground truth setting.
Meta Batch-Instance Normalization for Generalizable Person Re-Identification
To this end, we combine learnable batch-instance normalization layers with meta-learning and investigate the challenging cases caused by both batch and instance normalization layers.
Robust Long-Term Object Tracking via Improved Discriminative Model Prediction
We propose an improved discriminative model prediction method for robust long-term tracking based on a pre-trained short-term tracker.
Hi-CMD: Hierarchical Cross-Modality Disentanglement for Visible-Infrared Person Re-Identification
To implement our approach, we introduce an ID-preserving person image generation network and a hierarchical feature learning module.
RPM-Net: Robust Pixel-Level Matching Networks for Self-Supervised Video Object Segmentation
In this paper, we introduce a self-supervised approach for video object segmentation without human labeled data. Specifically, we present Robust Pixel-level Matching Net-works (RPM-Net), a novel deep architecture that matches pixels between adjacent frames, using only color information from unlabeled videos for training.
Learning to Align Multi-Camera Domains using Part-Aware Clustering for Unsupervised Video Person Re-Identification
Since the cost of labeling increases dramatically as the number of cameras increases, it is difficult to apply the re-identification algorithm to a large camera network.
Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection
We construct a structured domain adaptation framework for our learning paradigm and introduce a practical way of DD for implementation.
