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Found 11 papers, 4 papers with code
Algorithmic Data Minimization for Machine Learning over Internet-of-Things Data Streams
Machine learning can analyze vast amounts of data generated by IoT devices to identify patterns, make predictions, and enable real-time decision-making.
Generative Active Adaptation for Drifting and Imbalanced Network Intrusion Detection
We evaluate our end-to-end framework on both simulated IDS data and a real-world ISP dataset, demonstrating significant improvements in intrusion detection performance.
YOLOSCM: An improved YOLO algorithm for cars detection
To address the challenges of large-scale images and the non-uniform distribution of vehicles, we propose a Segmentation Clustering Module (SCM).
Research on Improved U-net Based Remote Sensing Image Segmentation Algorithm
In recent years, although U-Net network has made significant progress in the field of image segmentation, it still faces performance bottlenecks in remote sensing image segmentation.
ServeFlow: A Fast-Slow Model Architecture for Network Traffic Analysis
ServeFlow is able to make inferences on 76. 3% of flows in under 16ms, which is a speed-up of 40. 5x on the median end-to-end serving latency while increasing the service rate and maintaining similar accuracy.
Scale-Prior Deformable Convolution for Exemplar-Guided Class-Agnostic Counting
Here we propose a scale-sensitive generalized loss to tackle this problem.
Ranked #10 on
Object Counting
on FSC147
Probing Visual-Audio Representation for Video Highlight Detection via Hard-Pairs Guided Contrastive Learning
Our proposed method mainly leverages the intra-modality encoding and cross-modality co-occurrence encoding for fully representation modeling.
LEAF: Navigating Concept Drift in Cellular Networks
We then show that frequent model retraining with newly available data is not sufficient to mitigate concept drift, and can even degrade model accuracy further.
GroupFormer: Group Activity Recognition with Clustered Spatial-Temporal Transformer
It captures spatial-temporal contextual information jointly to augment the individual and group representations effectively with a clustered spatial-temporal transformer.
Video Crowd Localization with Multi-focus Gaussian Neighborhood Attention and a Large-Scale Benchmark
Video crowd localization is a crucial yet challenging task, which aims to estimate exact locations of human heads in the given crowded videos.
Rethinking Pseudo-LiDAR Representation
Based on this observation, we design an image based CNN detector named Patch-Net, which is more generalized and can be instantiated as pseudo-LiDAR based 3D detectors.
