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Found 14 papers, 10 papers with code
Few-shot Online Anomaly Detection and Segmentation
Recent research in this domain has emphasized the necessity of a large volume of training data, overlooking the practical scenario where, post-deployment of the model, unlabeled data containing both normal and abnormal samples can be utilized to enhance the model's performance.
Semi-supervised Counting via Pixel-by-pixel Density Distribution Modelling
This paper focuses on semi-supervised crowd counting, where only a small portion of the training data are labeled.
Gramformer: Learning Crowd Counting via Graph-Modulated Transformer
The graph is building upon the dissimilarities between patches, modulating the attention in an anti-similarity fashion.
Linguistic Profiling of Deepfakes: An Open Database for Next-Generation Deepfake Detection
The two distinguished features enable DFLIP-3K to develop a benchmark that promotes progress in linguistic profiling of deepfakes, which includes three sub-tasks namely deepfake detection, model identification, and prompt prediction.
Can SAM Count Anything? An Empirical Study on SAM Counting
Meta AI recently released the Segment Anything model (SAM), which has garnered attention due to its impressive performance in class-agnostic segmenting.
Remind of the Past: Incremental Learning with Analogical Prompts
It mimics the feature distribution of the target old class on the old model using only samples of new classes.
Towards Practical Multi-Robot Hybrid Tasks Allocation for Autonomous Cleaning
Task allocation plays a vital role in multi-robot autonomous cleaning systems, where multiple robots work together to clean a large area.
Isolation and Impartial Aggregation: A Paradigm of Incremental Learning without Interference
To avoid obvious stage learning bottlenecks, we propose a brand-new stage-isolation based incremental learning framework, which leverages a series of stage-isolated classifiers to perform the learning task of each stage without the interference of others.
Semi-supervised Crowd Counting via Density Agency
In this paper, we propose a new agency-guided semi-supervised counting approach.
Boosting Crowd Counting via Multifaceted Attention
Secondly, we design the Local Attention Regularization to supervise the training of LRA by minimizing the deviation among the attention for different feature locations.
Anomaly Detection via Self-organizing Map
Anomaly detection plays a key role in industrial manufacturing for product quality control.
Ranked #46 on
Anomaly Detection
on MVTec AD
Direct Measure Matching for Crowd Counting
Second, we derive a semi-balanced form of Sinkhorn divergence, based on which a Sinkhorn counting loss is designed for measure matching.
Towards a Universal Model for Cross-Dataset Crowd Counting
This paper proposes to handle the practical problem of learning a universal model for crowd counting across scenes and datasets.
Bayesian Loss for Crowd Count Estimation with Point Supervision
In crowd counting datasets, each person is annotated by a point, which is usually the center of the head.
