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20192026
most citedGhostNetV2: Enhance Cheap Operation with Long-Range Attention

278 citations · 465 across the 15 of their papers we have counts for

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27 papers · 1 filter

cs.CV2025

Circle-RoPE: Cone-like Decoupled Rotary Positional Embedding for Large Vision-Language Models

Chengcheng Wang, Jianyuan Guo, Hongguang Li +4

Rotary Position Embedding (RoPE) is widely adopted in large language models, but when applied to vision-language models (VLMs) it couples text and image position indices and can in…

cs.CV2023

LightCLIP: Learning Multi-Level Interaction for Lightweight Vision-Language Models

Ying Nie, Wei He, Kai Han +4

Vision-language pre-training like CLIP has shown promising performance on various downstream tasks such as zero-shot image classification and image-text retrieval. Most of the exis…

cs.CV202331 cited

One-for-All: Bridge the Gap Between Heterogeneous Architectures in Knowledge Distillation

Zhiwei Hao, Jianyuan Guo, Kai Han +4

Knowledge distillation~(KD) has proven to be a highly effective approach for enhancing model performance through a teacher-student training scheme. However, most existing distillat…

cs.CV2023

Gold-YOLO: Efficient Object Detector via Gather-and-Distribute Mechanism

Chengcheng Wang, Wei He, Ying Nie +4

In the past years, YOLO-series models have emerged as the leading approaches in the area of real-time object detection. Many studies pushed up the baseline to a higher level by mod…

cs.CV20231 cited

Category Feature Transformer for Semantic Segmentation

Quan Tang, Chuanjian Liu, Fagui Liu +5

Aggregation of multi-stage features has been revealed to play a significant role in semantic segmentation. Unlike previous methods employing point-wise summation or concatenation f…

cs.CV2022278 cited

GhostNetV2: Enhance Cheap Operation with Long-Range Attention

Yehui Tang, Kai Han, Jianyuan Guo +3

Light-weight convolutional neural networks (CNNs) are specially designed for applications on mobile devices with faster inference speed. The convolutional operation can only captur…