4 citations · 12 across the 17 of their papers we have counts for
10 papers · 1 filter
LLaVA-SLT: Visual Language Tuning for Sign Language Translation
Han Liang, Chengyu Huang, Yuecheng Xu +6
In the realm of Sign Language Translation (SLT), reliance on costly gloss-annotated datasets has posed a significant barrier. Recent advancements in gloss-free SLT methods have sho…
GAQAT: gradient-adaptive quantization-aware training for domain generalization
Jiacheng Jiang, Yuan Meng, Chen Tang +4
Research on loss surface geometry, such as Sharpness-Aware Minimization (SAM), shows that flatter minima improve generalization. Recent studies further reveal that flatter minima c…
RTF-Q: Efficient Unsupervised Domain Adaptation with Retraining-free Quantization
Nanyang Du, Chen Tang, Yuxiao Jiang +2
Performing unsupervised domain adaptation on resource-constrained edge devices is challenging. Existing research typically adopts architecture optimization (e.g., designing slimmab…
PRANCE: Joint Token-Optimization and Structural Channel-Pruning for Adaptive ViT Inference
Ye Li, Chen Tang, Yuan Meng +5
We introduce PRANCE, a Vision Transformer compression framework that jointly optimizes the activated channels and reduces tokens, based on the characteristics of inputs. Specifical…
Evaluating the Generalization Ability of Quantized LLMs: Benchmark, Analysis, and Toolbox
Yijun Liu, Yuan Meng, Fang Wu +7
Large language models (LLMs) have exhibited exciting progress in multiple scenarios, while the huge computational demands hinder their deployments in lots of real-world application…
Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers
Lei Chen, Yuan Meng, Chen Tang +5
Recent advancements in diffusion models, particularly the architectural transformation from UNet-based models to Diffusion Transformers (DiTs), significantly improve the quality an…
One QuantLLM for ALL: Fine-tuning Quantized LLMs Once for Efficient Deployments
Ke Yi, Yuhui Xu, Heng Chang +4
Large Language Models (LLMs) have advanced rapidly but face significant memory demands. While quantization has shown promise for LLMs, current methods typically require lengthy tra…
TMPQ-DM: Joint Timestep Reduction and Quantization Precision Selection for Efficient Diffusion Models
Haojun Sun, Chen Tang, Zhi Wang +4
Diffusion models have emerged as preeminent contenders in the realm of generative models. Distinguished by their distinctive sequential generative processes, characterized by hundr…
Investigating the Impact of Quantization on Adversarial Robustness
Qun Li, Yuan Meng, Chen Tang +2
Quantization is a promising technique for reducing the bit-width of deep models to improve their runtime performance and storage efficiency, and thus becomes a fundamental step for…
Retraining-free Model Quantization via One-Shot Weight-Coupling Learning
Chen Tang, Yuan Meng, Jiacheng Jiang +5
Quantization is of significance for compressing the over-parameterized deep neural models and deploying them on resource-limited devices. Fixed-precision quantization suffers from…