Effective whole-body pose estimation with two-stages distillation
Whole-body pose estimation localizes the human body, hand, face, and foot keypoints in an
image. This task is challenging due to multi-scale body parts, fine-grained localization for …
image. This task is challenging due to multi-scale body parts, fine-grained localization for …
A survey on efficient vision transformers: algorithms, techniques, and performance benchmarking
Vision Transformer (ViT) architectures are becoming increasingly popular and widely
employed to tackle computer vision applications. Their main feature is the capacity to extract …
employed to tackle computer vision applications. Their main feature is the capacity to extract …
Model quantization and hardware acceleration for vision transformers: A comprehensive survey
D Du, G Gong, X Chu - arXiv preprint arXiv:2405.00314, 2024 - arxiv.org
Vision Transformers (ViTs) have recently garnered considerable attention, emerging as a
promising alternative to convolutional neural networks (CNNs) in several vision-related …
promising alternative to convolutional neural networks (CNNs) in several vision-related …
Sparsity in transformers: A systematic literature review
Transformers have become the state-of-the-art architectures for various tasks in Natural
Language Processing (NLP) and Computer Vision (CV); however, their space and …
Language Processing (NLP) and Computer Vision (CV); however, their space and …
Efficient multimodal large language models: A survey
In the past year, Multimodal Large Language Models (MLLMs) have demonstrated
remarkable performance in tasks such as visual question answering, visual understanding …
remarkable performance in tasks such as visual question answering, visual understanding …
Effective Interplay between Sparsity and Quantization: From Theory to Practice
SB Harma, A Chakraborty, E Kostenok… - arXiv preprint arXiv …, 2024 - arxiv.org
The increasing size of deep neural networks necessitates effective model compression to
improve computational efficiency and reduce their memory footprint. Sparsity and …
improve computational efficiency and reduce their memory footprint. Sparsity and …
RCV2023 Challenges: Benchmarking Model Training and Inference for Resource-Constrained Deep Learning
This paper delves into the results of two resource-constrained deep learning challenges,
part of the workshop on Resource-Efficient Deep Learning for Computer Vision (RCV) at …
part of the workshop on Resource-Efficient Deep Learning for Computer Vision (RCV) at …
A Simple Low-bit Quantization Framework for Video Snapshot Compressive Imaging
Video Snapshot Compressive Imaging (SCI) aims to use a low-speed 2D camera to capture
high-speed scene as snapshot compressed measurements, followed by a reconstruction …
high-speed scene as snapshot compressed measurements, followed by a reconstruction …
From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks
Deep neural networks (DNNs) have been widely used in many artificial intelligence (AI)
tasks. However, deploying them brings significant challenges due to the huge cost of …
tasks. However, deploying them brings significant challenges due to the huge cost of …
Advances in Pruning and Quantization for Natural Language Processing
With ongoing advancements in natural language processing (NLP) and deep learning
methods, the demand for computational and memory resources has considerably increased …
methods, the demand for computational and memory resources has considerably increased …