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Found 8 papers, 2 papers with code
Mondrian: On-Device High-Performance Video Analytics with Compressive Packed Inference
In this paper, we present Mondrian, an edge system that enables high-performance object detection on high-resolution video streams.
Attention-Propagation Network for Egocentric Heatmap to 3D Pose Lifting
We propose a novel heatmap-to-3D lifting method composed of the Grid ViT Encoder and the Propagation Network.
Ego3DPose: Capturing 3D Cues from Binocular Egocentric Views
We propose a new perspective-aware representation using trigonometry, enabling the network to estimate the 3D orientation of limbs.
On the Importance of Critical Period in Multi-stage Reinforcement Learning
The initial years of an infant's life are known as the critical period, during which the overall development of learning performance is significantly impacted due to neural plasticity.
Toddler-Guidance Learning: Impacts of Critical Period on Multimodal AI Agents
To validate this hypothesis, we adapt this notion of critical periods to learning in AI agents and investigate the critical period in the virtual environment for AI agents.
VECA : A Toolkit for Building Virtual Environments to Train and Test Human-like Agents
To train and test human-like agents, we need an environment that imposes the agent to rich multimodal perception and allows comprehensive interactions for the agent, while also easily extensible to develop custom tasks.
Learning task-agnostic representation via toddler-inspired learning
One of the inherent limitations of current AI systems, stemming from the passive learning mechanisms (e. g., supervised learning), is that they perform well on labeled datasets but cannot deduce knowledge on their own.
BreathRNNet: Breathing Based Authentication on Resource-Constrained IoT Devices using RNNs
Increasing popularity of IoT devices makes a strong case for implementing RNN based inferences for applications such as acoustics based authentication, voice commands, and edge analytics for smart homes.
