11 citations · 24 across the 11 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…
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…
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…