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Found 12 papers, 8 papers with code
MobileNetV4 - Universal Models for the Mobile Ecosystem
We present the latest generation of MobileNets, known as MobileNetV4 (MNv4), featuring universally efficient architecture designs for mobile devices.
Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security
Next, we discuss several key challenges to achieve intelligent, efficient and secure Personal LLM Agents, followed by a comprehensive survey of representative solutions to address these challenges.
BiSwift: Bandwidth Orchestrator for Multi-Stream Video Analytics on Edge
To achieve this goal, we propose BiSwift, a bi-level framework that scales the concurrent real-time video analytics by a novel adaptive hybrid codec integrated with multi-level pipelines, and a global bandwidth controller for multiple video streams.
SwapMoE: Efficient Memory-Constrained Serving of Large Sparse MoE Models via Dynamic Expert Pruning and Swapping
The main idea of SwapMoE is to keep a small dynamic set of important experts, namely Virtual Experts, in the main memory for inference, while seamlessly maintaining how the Virtual Experts map to the actual experts.
ReMaX: Relaxing for Better Training on Efficient Panoptic Segmentation
This paper presents a new mechanism to facilitate the training of mask transformers for efficient panoptic segmentation, democratizing its deployment.
AccDecoder: Accelerated Decoding for Neural-enhanced Video Analytics
The quality of the video stream is key to neural network-based video analytics.
MOSAIC: Mobile Segmentation via decoding Aggregated Information and encoded Context
We present a next-generation neural network architecture, MOSAIC, for efficient and accurate semantic image segmentation on mobile devices.
Discovering Multi-Hardware Mobile Models via Architecture Search
Hardware-aware neural architecture designs have been predominantly focusing on optimizing model performance on single hardware and model development complexity, where another important factor, model deployment complexity, has been largely ignored.
FoodX-251: A Dataset for Fine-grained Food Classification
Food classification is a challenging problem due to the large number of categories, high visual similarity between different foods, as well as the lack of datasets for training state-of-the-art deep models.
Geo-Aware Networks for Fine-Grained Recognition
By leveraging geolocation information we improve top-1 accuracy in iNaturalist from 70. 1% to 79. 0% for a strong baseline image-only model.
Searching for MobileNetV3
We achieve new state of the art results for mobile classification, detection and segmentation.
Ranked #9 on
Dichotomous Image Segmentation
on DIS-TE1
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
We present a class of efficient models called MobileNets for mobile and embedded vision applications.
Ranked #238 on
Object Detection
on COCO test-dev
