activity
20162024
most citedFully Self-Supervised Learning for Semantic Segmentation

11 citations · 24 across the 11 of their papers we have counts for

collaborators

11 papers

cs.CV2024

Retraining-free Model Quantization via One-Shot Weight-Coupling Learning

Chen Tang, Yuan Meng, Jiacheng Jiang +5

Quantization is of significance for compressing the over-parameterized deep neural models and deploying them on resource-limited devices. Fixed-precision quantization suffers from…

cs.CV2023★ 2 cited

SEAM: Searching Transferable Mixed-Precision Quantization Policy through Large Margin Regularization

Chen Tang, Kai Ouyang, Zenghao Chai +4

Mixed-precision quantization (MPQ) suffers from the time-consuming process of searching the optimal bit-width allocation i.e., the policy) for each layer, especially when using lar…

cs.CV2022★ 1 cited

Arbitrary Bit-width Network: A Joint Layer-Wise Quantization and Adaptive Inference Approach

Chen Tang, Haoyu Zhai, Kai Ouyang +3

Conventional model quantization methods use a fixed quantization scheme to different data samples, which ignores the inherent "recognition difficulty" differences between various s…

cs.CV2022★ 1 cited

A Closer Look at Debiased Temporal Sentence Grounding in Videos: Dataset, Metric, and Approach

Xiaohan Lan, Yitian Yuan, Xin Wang +4

Temporal Sentence Grounding in Videos (TSGV), which aims to ground a natural language sentence in an untrimmed video, has drawn widespread attention over the past few years. Howeve…

cs.LG2022★ 5 cited

Mixed-Precision Neural Network Quantization via Learned Layer-wise Importance

Chen Tang, Kai Ouyang, Zhi Wang +4

The exponentially large discrete search space in mixed-precision quantization (MPQ) makes it hard to determine the optimal bit-width for each layer. Previous works usually resort t…

cs.CV2022★ 11 cited

Fully Self-Supervised Learning for Semantic Segmentation

Yuan Wang, Wei Zhuo, Yucong Li +3

In this work, we present a fully self-supervised framework for semantic segmentation(FS^4). A fully bootstrapped strategy for semantic segmentation, which saves efforts for the hug…

cs.CV2021★ 3 cited

A Survey on Temporal Sentence Grounding in Videos

Xiaohan Lan, Yitian Yuan, Xin Wang +2

Temporal sentence grounding in videos(TSGV), which aims to localize one target segment from an untrimmed video with respect to a given sentence query, has drawn increasing attentio…

cs.LG2021★ 1 cited

Online Continual Adaptation with Active Self-Training

Shiji Zhou, Han Zhao, Shanghang Zhang +4

Models trained with offline data often suffer from continual distribution shifts and expensive labeling in changing environments. This calls for a new online learning paradigm wher…

cs.MM2021

Multimedia Edge Computing

Zhi Wang, Wenwu Zhu, Lifeng Sun +6

In this paper, we investigate the recent studies on multimedia edge computing, from sensing not only traditional visual/audio data but also individuals' geographical preference and…

cs.CE2020

STSIR: Spatial Temporal Pandemic Model with Mobility Data

Wang Pan, Qipu Deng, Jiadong Li +2

With the outbreak of COVID-19, how to mitigate and suppress its spread is a big issue to the government. Department of public health need powerful models to model and predict the t…

cs.MM2016

Social- and Mobility-Aware Device-to-Device Content Delivery

Zhi Wang, Lifeng Sun, Miao Zhang +3

Mobile online social network services have seen a rapid increase, in which the huge amount of user-generated social media contents propagating between users via social connections…