Search Results for author:
Found 20 papers, 6 papers with code
Single-temporal Supervised Remote Change Detection for Domain Generalization
In this paper, we propose a multimodal contrastive learning (ChangeCLIP) based on visual-language pre-training for change detection domain generalization.
Leveraging Fine-Grained Information and Noise Decoupling for Remote Sensing Change Detection
Next, a shape-aware and a brightness-aware module are designed to improve the capacity for representation learning.
DMAD: Dual Memory Bank for Real-World Anomaly Detection
To address the challenge of real-world anomaly detection, we propose a new framework named Dual Memory bank enhanced representation learning for Anomaly Detection (DMAD).
Tuning-Free Image Customization with Image and Text Guidance
To our knowledge, this is the first tuning-free method that concurrently utilizes text and image guidance for image customization in specific regions.
Learning Unified Reference Representation for Unsupervised Multi-class Anomaly Detection
In the field of multi-class anomaly detection, reconstruction-based methods derived from single-class anomaly detection face the well-known challenge of ``learning shortcuts'', wherein the model fails to learn the patterns of normal samples as it should, opting instead for shortcuts such as identity mapping or artificial noise elimination.
PointSeg: A Training-Free Paradigm for 3D Scene Segmentation via Foundation Models
On top of that, PointSeg can incorporate with various segmentation models and even surpasses the supervised methods.
DiffuMatting: Synthesizing Arbitrary Objects with Matting-level Annotation
Our DiffuMatting shows several potential applications (e. g., matting-data generator, community-friendly art design and controllable generation).
UniM-OV3D: Uni-Modality Open-Vocabulary 3D Scene Understanding with Fine-Grained Feature Representation
3D open-vocabulary scene understanding aims to recognize arbitrary novel categories beyond the base label space.
Density Matters: Improved Core-set for Active Domain Adaptive Segmentation
Active domain adaptation has emerged as a solution to balance the expensive annotation cost and the performance of trained models in semantic segmentation.
Toward High Quality Facial Representation Learning
To improve the facial representation quality, we use feature map of a pre-trained visual backbone as a supervision item and use a partially pre-trained decoder for mask image modeling.
Stroke-based Neural Painting and Stylization with Dynamically Predicted Painting Region
To solve the problem, we propose Compositional Neural Painter, a novel stroke-based rendering framework which dynamically predicts the next painting region based on the current canvas, instead of dividing the image plane uniformly into painting regions.
FOLT: Fast Multiple Object Tracking from UAV-captured Videos Based on Optical Flow
Given the extracted flow, the flow-guided feature augmentation is designed to augment the object detection feature based on its optical flow, which improves the detection of small objects.
Multimodal Industrial Anomaly Detection via Hybrid Fusion
2D-based Industrial Anomaly Detection has been widely discussed, however, multimodal industrial anomaly detection based on 3D point clouds and RGB images still has many untouched fields.
Ranked #3 on
RGB+3D Anomaly Detection and Segmentation
on MVTEC 3D-AD
(using extra training data)
Contrastive Learning
RGB+3D Anomaly Detection and Segmentation
Learning from Noisy Labels with Coarse-to-Fine Sample Credibility Modeling
Specifically, for the clean set, we deliberately design a memory-based modulation scheme to dynamically adjust the contribution of each sample in terms of its historical credibility sequence during training, thus alleviating the effect from noisy samples incorrectly grouped into the clean set.
FRIH: Fine-grained Region-aware Image Harmonization
Image harmonization aims to generate a more realistic appearance of foreground and background for a composite image.
STC: Spatio-Temporal Contrastive Learning for Video Instance Segmentation
In contrast, we present a simple and efficient single-stage VIS framework based on the instance segmentation method CondInst by adding an extra tracking head.
Ranked #36 on
Video Instance Segmentation
on YouTube-VIS validation
SiamRCR: Reciprocal Classification and Regression for Visual Object Tracking
In addition, we add a localization branch to predict the localization accuracy, so that it can work as the replacement of the regression assistance link during inference.
Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation
In this paper, we address several inadequacies of current video object segmentation pipelines.
Dense Scene Multiple Object Tracking with Box-Plane Matching
Multiple Object Tracking (MOT) is an important task in computer vision.
Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking
Existing Multiple-Object Tracking (MOT) methods either follow the tracking-by-detection paradigm to conduct object detection, feature extraction and data association separately, or have two of the three subtasks integrated to form a partially end-to-end solution.
