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Found 19 papers, 10 papers with code
Confidence Regularized Self-Training
Recent advances in domain adaptation show that deep self-training presents a powerful means for unsupervised domain adaptation.
Ranked #16 on
Domain Adaptation
on VisDA2017
Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training
In this paper, we propose a novel UDA framework based on an iterative self-training procedure, where the problem is formulated as latent variable loss minimization, and can be solved by alternatively generating pseudo labels on target data and re-training the model with these labels.
Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training
Recent deep networks achieved state of the art performanceon a variety of semantic segmentation tasks.
WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation
Visual anomaly classification and segmentation are vital for automating industrial quality inspection.
Ranked #9 on
Anomaly Detection
on VisA
Simultaneous Edge Alignment and Learning
Edge detection is among the most fundamental vision problems for its role in perceptual grouping and its wide applications.
SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation
Visual anomaly detection is commonly used in industrial quality inspection.
Ranked #15 on
Anomaly Detection
on VisA
(Detection AUROC metric)
Joint Disentangling and Adaptation for Cross-Domain Person Re-Identification
To this end, we propose a joint learning framework that disentangles id-related/unrelated features and enforces adaptation to work on the id-related feature space exclusively.
Ranked #6 on
Unsupervised Domain Adaptation
on Market to MSMT
Comprehensive Attention Self-Distillation for Weakly-Supervised Object Detection
Moreover, the image-level category labels do not enforce consistent object detection across different transformations of the same images.
Ranked #1 on
Weakly Supervised Object Detection
on MSCOCO
Unsupervised Domain Adaptation via Calibrating Uncertainties
Unsupervised domain adaptation (UDA) aims at inferring class labels for unlabeled target domain given a related labeled source dataset.
Hard Class Rectification for Domain Adaptation
Second, we further consider that the predictions of target samples belonging to the hard class are vulnerable to perturbations.
Semi-supervised Domain Adaptation
Unsupervised Domain Adaptation
Scale Optimization for Full-Image-CNN Vehicle Detection
By selecting better scales in the region proposal input and by combining feature maps through careful design of the convolutional neural network, we improve performance on smaller objects.
Sliced Wasserstein Kernels for Probability Distributions
Optimal transport distances, otherwise known as Wasserstein distances, have recently drawn ample attention in computer vision and machine learning as a powerful discrepancy measure for probability distributions.
Deep Classification Network for Monocular Depth Estimation
Monocular Depth Estimation is usually treated as a supervised and regression problem when it actually is very similar to semantic segmentation task since they both are fundamentally pixel-level classification tasks.
Conservative Wasserstein Training for Pose Estimation
We propose to incorporate inter-class correlations in a Wasserstein training framework by pre-defining ($i. e.,$ using arc length of a circle) or adaptively learning the ground metric.
Privacy Analysis of Deep Learning in the Wild: Membership Inference Attacks against Transfer Learning
One major privacy attack in this domain is membership inference, where an adversary aims to determine whether a target data sample is part of the training set of a target ML model.
MotionInput v2.0 supporting DirectX: A modular library of open-source gesture-based machine learning and computer vision methods for interacting and controlling existing software with a webcam
We also introduce a series of bespoke gesture recognition classifications as DirectInput triggers, including gestures for idle states, auto calibration, depth capture from a 2D RGB webcam stream and tracking of facial motions such as mouth motions, winking, and head direction with rotation.
From Text to Pixels: A Context-Aware Semantic Synergy Solution for Infrared and Visible Image Fusion
With the rapid progression of deep learning technologies, multi-modality image fusion has become increasingly prevalent in object detection tasks.
FairRAG: Fair Human Generation via Fair Retrieval Augmentation
In this work, we introduce Fair Retrieval Augmented Generation (FairRAG), a novel framework that conditions pre-trained generative models on reference images retrieved from an external image database to improve fairness in human generation.
On the Scalability of Diffusion-based Text-to-Image Generation
On the data scaling side, we show the quality and diversity of the training set matters more than simply dataset size.
