Search Results for author:
Found 7 papers, 3 papers with code
HawkVision: Low-Latency Modeless Edge AI Serving
The trend of modeless ML inference is increasingly growing in popularity as it hides the complexity of model inference from users and caters to diverse user and application accuracy requirements.
Cross-head Supervision for Crowd Counting with Noisy Annotations
To alleviate the negative impact of noisy annotations, we propose a novel crowd counting model with one convolution head and one transformer head, in which these two heads can supervise each other in noisy areas, called Cross-Head Supervision.
Unsupervised Video Anomaly Detection for Stereotypical Behaviours in Autism
Correspondingly, we propose a Dual Stream deep model for Stereotypical Behaviours Detection, DS-SBD, based on the temporal trajectory of human poses and the repetition patterns of human actions.
Motion Matters: A Novel Motion Modeling For Cross-View Gait Feature Learning
Specifically, we first extract the motion features from the encoded motion sequences in the shallow layer.
Forget Less, Count Better: A Domain-Incremental Self-Distillation Learning Benchmark for Lifelong Crowd Counting
Crowd counting has important applications in public safety and pandemic control.
Deep Rank-Consistent Pyramid Model for Enhanced Crowd Counting
Specifically, we propose a Deep Rank-consistEnt pyrAmid Model (DREAM), which makes full use of rank consistency across coarse-to-fine pyramid features in latent spaces for enhanced crowd counting with massive unlabeled images.
Cheetah: Accelerating Database Queries with Switch Pruning
In this paper, we leverage programmable switches in the network to partially offload query computation to the switch.
Databases Networking and Internet Architecture
