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20202023
most citedLocally Free Weight Sharing for Network Width Search

23 citations · 49 across the 9 of their papers we have counts for

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9 papers · 1 filter

cs.CV202310 cited

Detecting Any Human-Object Interaction Relationship: Universal HOI Detector with Spatial Prompt Learning on Foundation Models

Yichao Cao, Qingfei Tang, Xiu Su +4

Human-object interaction (HOI) detection aims to comprehend the intricate relationships between humans and objects, predicting triplets, and serving as th…

cs.CV20231 cited

CoNe: Contrast Your Neighbours for Supervised Image Classification

Mingkai Zheng, Shan You, Lang Huang +5

Image classification is a longstanding problem in computer vision and machine learning research. Most recent works (e.g. SupCon , Triplet, and max-margin) mainly focus on grouping…

cs.CV2023

Re-mine, Learn and Reason: Exploring the Cross-modal Semantic Correlations for Language-guided HOI detection

Yichao Cao, Qingfei Tang, Feng Yang +4

Human-Object Interaction (HOI) detection is a challenging computer vision task that requires visual models to address the complex interactive relationship between humans and object…

cs.CV20222 cited

Searching for Network Width with Bilaterally Coupled Network

Xiu Su, Shan You, Jiyang Xie +4

Searching for a more compact network width recently serves as an effective way of channel pruning for the deployment of convolutional neural networks (CNNs) under hardware constrai…

cs.CV20216 cited

K-shot NAS: Learnable Weight-Sharing for NAS with K-shot Supernets

Xiu Su, Shan You, Mingkai Zheng +4

In one-shot weight sharing for NAS, the weights of each operation (at each layer) are supposed to be identical for all architectures (paths) in the supernet. However, this rules ou…

cs.CV20212 cited

BCNet: Searching for Network Width with Bilaterally Coupled Network

Xiu Su, Shan You, Fei Wang +3

Searching for a more compact network width recently serves as an effective way of channel pruning for the deployment of convolutional neural networks (CNNs) under hardware constrai…