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20222025
most citedEfficientRep:An Efficient Repvgg-style ConvNets with Hardware-aware Neural Network Design

24 citations · 26 across the 7 of their papers we have counts for

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7 papers · 1 filter

cs.CV2025

Omni-Dish: Photorealistic and Faithful Image Generation and Editing for Arbitrary Chinese Dishes

Huijie Liu, Bingcan Wang, Jie Hu +2

Dish images play a crucial role in the digital era, with the demand for culturally distinctive dish images continuously increasing due to the digitization of the food industry and…

cs.CV2024

Faster Multi-GPU Training with PPLL: A Pipeline Parallelism Framework Leveraging Local Learning

Xiuyuan Guo, Chengqi Xu, Guinan Guo +6

Currently, training large-scale deep learning models is typically achieved through parallel training across multiple GPUs. However, due to the inherent communication overhead and s…

cs.CV20241 cited

Kangaroo: A Powerful Video-Language Model Supporting Long-context Video Input

Jiajun Liu, Yibing Wang, Hanghang Ma +6

Rapid advancements have been made in extending Large Language Models (LLMs) to Large Multi-modal Models (LMMs). However, extending input modality of LLMs to video data remains a ch…

cs.CV2024

Fine-gained Zero-shot Video Sampling

Dengsheng Chen, Jie Hu, Xiaoming Wei +1

Incorporating a temporal dimension into pretrained image diffusion models for video generation is a prevalent approach. However, this method is computationally demanding and necess…

cs.CV202324 cited

EfficientRep:An Efficient Repvgg-style ConvNets with Hardware-aware Neural Network Design

Kaiheng Weng, Xiangxiang Chu, Xiaoming Xu +2

We present a hardware-efficient architecture of convolutional neural network, which has a repvgg-like architecture. Flops or parameters are traditional metrics to evaluate the effi…

cs.CV2022

PPMN: Pixel-Phrase Matching Network for One-Stage Panoptic Narrative Grounding

Zihan Ding, Zi-han Ding, Tianrui Hui +4

Panoptic Narrative Grounding (PNG) is an emerging task whose goal is to segment visual objects of things and stuff categories described by dense narrative captions of a still image…