344 citations · 513 across the 16 of their papers we have counts for
17 papers
From 2D Grids to 1D Tokens: Reforming Shared Representations for Multimodal Image Fusion
Yuchen Xian, Yunqiu Xu, Yang He +1
Multimodal image fusion aims to integrate complementary information from different modalities into a fused image that preserves rich local details while maintaining globally consis…
VIA-SD: Verification via Intra-Model Routing for Speculative Decoding
Yuchen Xian, Yang He, Yunqiu Xu +1
Speculative decoding (SD) addresses the high inference costs of LLMs by having lightweight drafters generate candidates for large verifiers to validate in parallel. Existing draft-…
Soft Label Pruning and Quantization for Large-Scale Dataset Distillation
Xiao Lingao, Yang He
Large-scale dataset distillation requires storing auxiliary soft labels that can be 30-40x larger on ImageNet-1K and 200x larger on ImageNet-21K than the condensed images, undermin…
Dataset Color Quantization: A Training-Oriented Framework for Dataset-Level Compression
Chenyue Yu, Lingao Xiao, Jinhong Deng +2
Large-scale image datasets are fundamental to deep learning, but their high storage demands pose challenges for deployment in resource-constrained environments. While existing appr…
SCOPE: Saliency-Coverage Oriented Token Pruning for Efficient Multimodel LLMs
Jinhong Deng, Wen Li, Joey Tianyi Zhou +1
Multimodal Large Language Models (MLLMs) typically process a large number of visual tokens, leading to considerable computational overhead, even though many of these tokens are red…
Training-Free Dataset Pruning for Instance Segmentation
Yalun Dai, Lingao Xiao, Ivor W. Tsang +1
Existing dataset pruning techniques primarily focus on classification tasks, limiting their applicability to more complex and practical tasks like instance segmentation. Instance s…
Swift Cross-Dataset Pruning: Enhancing Fine-Tuning Efficiency in Natural Language Understanding
Binh-Nguyen Nguyen, Yang He
Dataset pruning aims to select a subset of a dataset for efficient model training. While data efficiency in natural language processing has primarily focused on within-corpus scena…
Are Large-scale Soft Labels Necessary for Large-scale Dataset Distillation?
Lingao Xiao, Yang He
In ImageNet-condensation, the storage for auxiliary soft labels exceeds that of the condensed dataset by over 30 times. However, are large-scale soft labels necessary for large-sca…
Data-independent Module-aware Pruning for Hierarchical Vision Transformers
Yang He, Joey Tianyi Zhou
Hierarchical vision transformers (ViTs) have two advantages over conventional ViTs. First, hierarchical ViTs achieve linear computational complexity with respect to image size by l…
Multisize Dataset Condensation
Yang He, Lingao Xiao, Joey Tianyi Zhou +1
While dataset condensation effectively enhances training efficiency, its application in on-device scenarios brings unique challenges. 1) Due to the fluctuating computational resour…
You Only Condense Once: Two Rules for Pruning Condensed Datasets
Yang He, Lingao Xiao, Joey Tianyi Zhou
Dataset condensation is a crucial tool for enhancing training efficiency by reducing the size of the training dataset, particularly in on-device scenarios. However, these scenarios…
Structured Pruning for Deep Convolutional Neural Networks: A survey
Yang He, Lingao Xiao
The remarkable performance of deep Convolutional neural networks (CNNs) is generally attributed to their deeper and wider architectures, which can come with significant computation…
Progressive Local Filter Pruning for Image Retrieval Acceleration
Xiaodong Wang, Zhedong Zheng, Yang He +3
This paper focuses on network pruning for image retrieval acceleration. Prevailing image retrieval works target at the discriminative feature learning, while little attention is pa…
Filter Pruning by Switching to Neighboring CNNs with Good Attributes
Yang He, Ping Liu, Linchao Zhu +1
Filter pruning is effective to reduce the computational costs of neural networks. Existing methods show that updating the previous pruned filter would enable large model capacity a…
Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration
Yang He, Ping Liu, Ziwei Wang +2
Previous works utilized ''smaller-norm-less-important'' criterion to prune filters with smaller norm values in a convolutional neural network. In this paper, we analyze this norm-b…
Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks
Yang He, Guoliang Kang, Xuanyi Dong +2
This paper proposed a Soft Filter Pruning (SFP) method to accelerate the inference procedure of deep Convolutional Neural Networks (CNNs). Specifically, the proposed SFP enables th…
Asymptotic Soft Filter Pruning for Deep Convolutional Neural Networks
Yang He, Xuanyi Dong, Guoliang Kang +3
Deeper and wider Convolutional Neural Networks (CNNs) achieve superior performance but bring expensive computation cost. Accelerating such over-parameterized neural network has rec…