15 citations · 20 across the 11 of their papers we have counts for
11 papers
ATA: Bridging Implicit Reasoning with Attention-Guided and Action-Guided Inference for Vision-Language Action Models
Cheng Yang, Jianhao Jiao, Lingyi Huang +8
Vision-Language-Action (VLA) models rely on current observations, including images, language instructions, and robot states, to predict actions and complete tasks. While accurate v…
InstructMoLE: Instruction-Guided Mixture of Low-rank Experts for Multi-Conditional Image Generation
Jinqi Xiao, Qing Yan, Liming Jiang +8
Parameter-Efficient Fine-Tuning of Diffusion Transformers (DiTs) for diverse, multi-conditional tasks often suffers from task interference when using monolithic adapters like LoRA.…
EcoSpa: Efficient Transformer Training with Coupled Sparsity
Jinqi Xiao, Cheng Luo, Lingyi Huang +8
Transformers have become the backbone of modern AI, yet their high computational demands pose critical system challenges. While sparse training offers efficiency gains, existing me…
TopV: Compatible Token Pruning with Inference Time Optimization for Fast and Low-Memory Multimodal Vision Language Model
Cheng Yang, Yang Sui, Jinqi Xiao +8
Vision-Language Models (VLMs) demand substantial computational resources during inference, largely due to the extensive visual input tokens for representing visual information. Pre…
HeadInfer: Memory-Efficient LLM Inference by Head-wise Offloading
Cheng Luo, Zefan Cai, Hanshi Sun +7
Transformer-based large language models (LLMs) demonstrate impressive performance in long context generation. Extending the context length has disproportionately shifted the memory…
COAP: Memory-Efficient Training with Correlation-Aware Gradient Projection
Jinqi Xiao, Shen Sang, Tiancheng Zhi +5
Training large-scale neural networks in vision, and multimodal domains demands substantial memory resources, primarily due to the storage of optimizer states. While LoRA, a popular…
MoE-I: Compressing Mixture of Experts Models through Inter-Expert Pruning and Intra-Expert Low-Rank Decomposition
Cheng Yang, Yang Sui, Jinqi Xiao +7
The emergence of Mixture of Experts (MoE) LLMs has significantly advanced the development of language models. Compared to traditional LLMs, MoE LLMs outperform traditional LLMs by…
DisDet: Exploring Detectability of Backdoor Attack on Diffusion Models
Yang Sui, Huy Phan, Jinqi Xiao +6
In the exciting generative AI era, the diffusion model has emerged as a very powerful and widely adopted content generation and editing tool for various data modalities, making the…
ELRT: Efficient Low-Rank Training for Compact Convolutional Neural Networks
Yang Sui, Miao Yin, Yu Gong +3
Low-rank compression, a popular model compression technique that produces compact convolutional neural networks (CNNs) with low rankness, has been well-studied in the literature. O…
COMCAT: Towards Efficient Compression and Customization of Attention-Based Vision Models
Jinqi Xiao, Miao Yin, Yu Gong +3
Attention-based vision models, such as Vision Transformer (ViT) and its variants, have shown promising performance in various computer vision tasks. However, these emerging archite…
HALOC: Hardware-Aware Automatic Low-Rank Compression for Compact Neural Networks
Jinqi Xiao, Chengming Zhang, Yu Gong +5
Low-rank compression is an important model compression strategy for obtaining compact neural network models. In general, because the rank values directly determine the model comple…