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Found 7 papers, 7 papers with code
Efficient Online Processing with Deep Neural Networks
The capabilities and adoption of deep neural networks (DNNs) grow at an exhilarating pace: Vision models accurately classify human actions in videos and identify cancerous tissue in medical scans as precisely than human experts; large language models answer wide-ranging questions, generate code, and write prose, becoming the topic of everyday dinner-table conversations.
Structured Pruning Adapters
To improve on this, we propose Structured Pruning Adapters (SPAs), a family of compressing, task-switching network adapters, that accelerate and specialize networks using tiny parameter sets and structured pruning.
Continual Inference: A Library for Efficient Online Inference with Deep Neural Networks in PyTorch
We present Continual Inference, a Python library for implementing Continual Inference Networks (CINs) in PyTorch, a class of Neural Networks designed specifically for efficient inference in both online and batch processing scenarios.
Continual Spatio-Temporal Graph Convolutional Networks
Graph-based reasoning over skeleton data has emerged as a promising approach for human action recognition.
Ranked #7 on
Skeleton Based Action Recognition
on Kinetics-Skeleton dataset
(GFLOPS per prediction metric)
Continual Transformers: Redundancy-Free Attention for Online Inference
Transformers in their common form are inherently limited to operate on whole token sequences rather than on one token at a time.
Ranked #4 on
Online Action Detection
on TVSeries
Continual 3D Convolutional Neural Networks for Real-time Processing of Videos
We introduce Continual 3D Convolutional Neural Networks (Co3D CNNs), a new computational formulation of spatio-temporal 3D CNNs, in which videos are processed frame-by-frame rather than by clip.
Ranked #42 on
Action Classification
on Charades
Supervised Domain Adaptation: A Graph Embedding Perspective and a Rectified Experimental Protocol
Domain Adaptation is the process of alleviating distribution gaps between data from different domains.
Ranked #27 on
Domain Adaptation
on Office-31
