6 citations · 13 across the 14 of their papers we have counts for
14 papers
AGVBench: A Reliability-Oriented Benchmark of Data Augmentation for Vein Recognition
Haiyang Li, Yuming Fu, Qun Song +6
Vein recognition is a secure biometric technology often constrained by limited annotated data and imaging variations. While data augmentation mitigates this, strategies designed fo…
OmniOpt: Taxonomy, Geometry, and Benchmarking of Modern Optimizers
Siyuan Li, Jiabao Pan, Yumou Liu +9
Optimizer selection for large-scale model training has become a system-level design decision constrained jointly by compute, memory, tuning budget, and task diversity, yet the land…
EarlyTom: Early Token Compression Completes Fast Video Understanding
Hesong Wang, Xin Jin, Lu Lu +4
Video large language models (Video-LLMs) have demonstrated strong capabilities in video understanding tasks. However, their practical deployment is still hindered by the inefficien…
LVOmniBench: Pioneering Long Audio-Video Understanding Evaluation for Omnimodal LLMs
Keda Tao, Yuhua Zheng, Jia Xu +13
Recent advancements in omnimodal large language models (OmniLLMs) have significantly improved the comprehension of audio and video inputs. However, current evaluations primarily fo…
MergeMix: A Unified Augmentation Paradigm for Visual and Multi-Modal Understanding
Xin Jin, Siyuan Li, Siyong Jian +2
Vision-language alignment in multi-modal large language models (MLLMs) relies on supervised fine-tuning (SFT) or reinforcement learning (RL). To align multi-modal large language mo…
MS-Mix: Sentiment-Guided Adaptive Augmentation for Multimodal Sentiment Analysis
Hongyu Zhu, Lin Chen, Xin Jin +1
Multimodal Sentiment Analysis (MSA) integrates complementary features from text, video, and audio for robust emotion understanding in human interactions. However, models suffer fro…
Taming LLMs by Scaling Learning Rates with Gradient Grouping
Siyuan Li, Juanxi Tian, Zedong Wang +4
Training large language models (LLMs) poses challenges due to their massive scale and heterogeneous architectures. While adaptive optimizers like AdamW help address gradient variat…
Relax DARTS: Relaxing the Constraints of Differentiable Architecture Search for Eye Movement Recognition
Hongyu Zhu, Xin Jin, Hongchao Liao +3
Eye movement biometrics is a secure and innovative identification method. Deep learning methods have shown good performance, but their network architecture relies on manual design…
EM-DARTS: Hierarchical Differentiable Architecture Search for Eye Movement Recognition
Huafeng Qin, Hongyu Zhu, Xin Jin +3
Eye movement biometrics has received increasing attention thanks to its highly secure identification. Although deep learning (DL) models have shown success in eye movement recognit…
A Survey on Mixup Augmentations and Beyond
Xin Jin, Hongyu Zhu, Siyuan Li +6
As Deep Neural Networks have achieved thrilling breakthroughs in the past decade, data augmentations have garnered increasing attention as regularization techniques when massive la…
SUMix: Mixup with Semantic and Uncertain Information
Huafeng Qin, Xin Jin, Hongyu Zhu +3
Mixup data augmentation approaches have been applied for various tasks of deep learning to improve the generalization ability of deep neural networks. Some existing approaches CutM…
StarLKNet: Star Mixup with Large Kernel Networks for Palm Vein Identification
Xin Jin, Hongyu Zhu, Mounîm A. El Yacoubi +4
As a representative of a new generation of biometrics, vein identification technology offers a high level of security and convenience.Convolutional neural networks (CNNs), a promin…
EmMixformer: Mix transformer for eye movement recognition
Huafeng Qin, Hongyu Zhu, Xin Jin +3
Eye movement (EM) is a new highly secure biometric behavioral modality that has received increasing attention in recent years. Although deep neural networks, such as convolutional…
Adversarial AutoMixup
Huafeng Qin, Xin Jin, Yun Jiang +2
Data mixing augmentation has been widely applied to improve the generalization ability of deep neural networks. Recently, offline data mixing augmentation, e.g. handcrafted and sal…