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Found 11 papers, 4 papers with code
Towards Training-free Anomaly Detection with Vision and Language Foundation Models
Anomaly detection is valuable for real-world applications, such as industrial quality inspection.
ONER: Online Experience Replay for Incremental Anomaly Detection
Specifically, our framework utilizes two types of experiences from past tasks: decomposed prompts and semantic prototypes, addressing both model parameter updates and feature optimization.
Unilaterally Aggregated Contrastive Learning with Hierarchical Augmentation for Anomaly Detection
Anomaly detection (AD), aiming to find samples that deviate from the training distribution, is essential in safety-critical applications.
Align-DETR: Enhancing End-to-end Object Detection with Aligned Loss
To tackle both issues simultaneously, we introduce a novel loss function, termed as Align Loss, designed to resolve the discrepancy between the two tasks.
Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw Puzzles
Video Anomaly Detection (VAD) is an important topic in computer vision.
Ranked #7 on
Anomaly Detection
on ShanghaiTech
Indirect-Instant Attention Optimization for Crowd Counting in Dense Scenes
One of appealing approaches to guiding learnable parameter optimization, such as feature maps, is global attention, which enlightens network intelligence at a fraction of the cost.
Learning Oriented Remote Sensing Object Detection via Naive Geometric Computing
Detecting oriented objects along with estimating their rotation information is one crucial step for analyzing remote sensing images.
Student-Teacher Feature Pyramid Matching for Anomaly Detection
Anomaly detection is a challenging task and usually formulated as an one-class learning problem for the unexpectedness of anomalies.
Ranked #34 on
Anomaly Detection
on VisA
A Novel Video Salient Object Detection Method via Semi-supervised Motion Quality Perception
Consequently, we can achieve a significant performance improvement by using this new training set to start a new round of network training.
An Automated Auto-encoder Correlation-based Health-Monitoring and Prognostic Method for Machine Bearings
We demonstrate the superiority of the AEC over many other state-of-the-art approaches for the health monitoring and prognostic of machine bearings.
Blurred Palmprint Recognition Based on Stable-FeatureExtraction Using a Vese-Osher Decomposition Model
The experimental results are used to demonstrate the theoretica conclusion that the structure laver is stable foidifferent bluring scales The WRHOG method also proves to be an advanced and robust method of distinauishing blurredpalmprints.
