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06/30/2023
Stitched ViTs are Flexible Vision Backbones
Large pretrained plain vision Transformers (ViTs) have been the workhors...
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05/28/2023
Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning
Large pre-trained models (LPMs), such as LLaMA and ViT-G, have shown exc...
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05/18/2023
PTQD: Accurate Post-Training Quantization for Diffusion Models
Diffusion models have recently dominated image synthesis and other relat...
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02/13/2023
Stitchable Neural Networks
The public model zoo containing enormous powerful pretrained model famil...
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02/02/2023
A Survey on Efficient Training of Transformers
Recent advances in Transformers have come with a huge requirement on com...
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11/14/2022
BiViT: Extremely Compressed Binary Vision Transformer
Model binarization can significantly compress model size, reduce energy ...
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09/19/2022
EcoFormer: Energy-Saving Attention with Linear Complexity
Transformer is a transformative framework that models sequential data an...
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08/23/2022
FocusFormer: Focusing on What We Need via Architecture Sampler
Vision Transformers (ViTs) have underpinned the recent breakthroughs in ...
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07/21/2022
An Efficient Spatio-Temporal Pyramid Transformer for Action Detection
The task of action detection aims at deducing both the action category a...
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05/26/2022
Fast Vision Transformers with HiLo Attention
Vision Transformers (ViTs) have triggered the most recent and significan...
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03/28/2022
Automated Progressive Learning for Efficient Training of Vision Transformers
Recent advances in vision Transformers (ViTs) have come with a voracious...
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11/24/2021
Sharpness-aware Quantization for Deep Neural Networks
Network quantization is an effective compression method to reduce the mo...
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11/22/2021
Mesa: A Memory-saving Training Framework for Transformers
There has been an explosion of interest in designing high-performance Tr...
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08/03/2021
Elastic Architecture Search for Diverse Tasks with Different Resources
We study a new challenging problem of efficient deployment for diverse t...
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05/29/2021
Less is More: Pay Less Attention in Vision Transformers
Transformers have become one of the dominant architectures in deep learn...
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05/04/2021
End-to-end One-shot Human Parsing
Previous human parsing models are limited to parsing humans into pre-def...
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03/19/2021
Scalable Visual Transformers with Hierarchical Pooling
The recently proposed Visual image Transformers (ViT) with pure attentio...
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01/13/2021
ABS: Automatic Bit Sharing for Model Compression
We present Automatic Bit Sharing (ABS) to automatically search for optim...
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11/29/2020
Fully Quantized Image Super-Resolution Networks
With the rising popularity of intelligent mobile devices, it is of great...
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08/12/2020
FATNN: Fast and Accurate Ternary Neural Networks
Ternary Neural Networks (TNNs) have received much attention due to being...
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07/14/2020
AQD: Towards Accurate Quantized Object Detection
Network quantization aims to lower the bitwidth of weights and activatio...
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04/19/2020
Role-Wise Data Augmentation for Knowledge Distillation
Knowledge Distillation (KD) is a common method for transferring the “kno...
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03/07/2020
Generative Low-bitwidth Data Free Quantization
Neural network quantization is an effective way to compress deep models ...
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02/07/2020
Switchable Precision Neural Networks
Instantaneous and on demand accuracy-efficiency trade-off has been recen...
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02/03/2020
Automatic Pruning for Quantized Neural Networks
Neural network quantization and pruning are two techniques commonly used...
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01/04/2020
Discrimination-aware Network Pruning for Deep Model Compression
We study network pruning which aims to remove redundant channels/kernels...
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09/22/2019
Structured Binary Neural Networks for Image Recognition
We propose methods to train convolutional neural networks (CNNs) with bo...
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09/05/2019
Training Compact Neural Networks via Auxiliary Overparameterization
It is observed that overparameterization (i.e., designing neural network...
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08/21/2019
RBCN: Rectified Binary Convolutional Networks for Enhancing the Performance of 1-bit DCNNs
Binarized convolutional neural networks (BCNNs) are widely used to impro...
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08/10/2019
Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations
This paper tackles the problem of training a deep convolutional neural n...
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03/27/2019
Training Quantized Network with Auxiliary Gradient Module
In this paper, we seek to tackle two challenges in training low-precisio...
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11/22/2018
Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation
In this paper, we propose to train convolutional neural networks (CNNs) ...
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11/22/2018
Rethinking Binary Neural Network for Accurate Image Classification and Semantic Segmentation
In this paper, we propose to train a network with both binary weights an...
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10/28/2018
Discrimination-aware Channel Pruning for Deep Neural Networks
Channel pruning is one of the predominant approaches for deep model comp...
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08/08/2018
Training Compact Neural Networks with Binary Weights and Low Precision Activations
In this paper, we propose to train a network with binary weights and low...
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11/17/2017
Parallel Attention: A Unified Framework for Visual Object Discovery through Dialogs and Queries
Recognising objects according to a pre-defined fixed set of class labels...
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11/01/2017
Towards Effective Low-bitwidth Convolutional Neural Networks
This paper tackles the problem of training a deep convolutional neural n...
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07/07/2017
TasselNet: Counting maize tassels in the wild via local counts regression network
Accurately counting maize tassels is important for monitoring the growth...
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05/28/2017
Care about you: towards large-scale human-centric visual relationship detection
Visual relationship detection aims to capture interactions between pairs...
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03/18/2017
Towards Context-aware Interaction Recognition
Recognizing how objects interact with each other is a crucial task in vi...
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11/30/2016
Sequential Person Recognition in Photo Albums with a Recurrent Network
Recognizing the identities of people in everyday photos is still a very ...
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11/30/2016
Attend in groups: a weakly-supervised deep learning framework for learning from web data
Large-scale datasets have driven the rapid development of deep neural ne...
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07/27/2016
Visual Tracking via Shallow and Deep Collaborative Model
In this paper, we propose a robust tracking method based on the collabor...
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03/09/2016
