2 citations · 2 across the 14 of their papers we have counts for
15 papers
MMArt: A Multi-Perspective Multimodal Dataset for Visual Art Understanding
Shuai Wang, Wangyuan Ding, Yixian Shen +5
Recent vision-language models demonstrate impressive general visual understanding, yet their art interpretation remains shallow: they describe surface content but struggle with for…
Spectral-Progressive Thought Flow for Lightweight Multimodal Reasoning
Yixian Shen, Zhiheng Yang, Qi Bi +6
Multimodal spatial reasoning often relies on long chains of intermediate textual and visual thoughts, where accumulating visual tokens and dense cross-modal attention incur substan…
Fast Transformer Inference on ARM-Based HMPSoCs
Hang Xu, Yixian Shen, Thanassis Giannetsos +1
Transformer models have set new performance standards for machine learning (ML) tasks. However, their resource-intensive deployment on resource-constrained edge devices for cloud-f…
A-MAR: Agent-based Multimodal Art Retrieval for Fine-Grained Artwork Understanding
Shuai Wang, Hongyi Zhu, Jia-Hong Huang +6
Understanding artworks requires multi-step reasoning over visual content and cultural, historical, and stylistic context. While recent multimodal large language models show promise…
A Novel Automatic Framework for Speaker Drift Detection in Synthesized Speech
Jia-Hong Huang, Seulgi Kim, Yi Chieh Liu +5
Recent diffusion-based text-to-speech (TTS) models achieve high naturalness and expressiveness, yet often suffer from speaker drift, a subtle, gradual shift in perceived speaker id…
Keeping the Evidence Chain: Semantic Evidence Allocation for Training-Free Token Pruning in Video Temporal Grounding
Jiaqi Li, Shuntian Zheng, Yixian Shen +4
Video Temporal Grounding (VTG) localizes the temporal boundaries of query-relevant moments in long, untrimmed videos, making video-language-model prohibitively expensive. While rec…
NeuroAda: Activating Each Neuron's Potential for Parameter-Efficient Fine-Tuning
Zhi Zhang, Yixian Shen, Congfeng Cao +1
Existing parameter-efficient fine-tuning (PEFT) methods primarily fall into two categories: addition-based and selective in-situ adaptation. The former, such as LoRA, introduce add…
SurveyGen-I: Consistent Scientific Survey Generation with Evolving Plans and Memory-Guided Writing
Jing Chen, Zhiheng Yang, Yixian Shen +4
Survey papers play a critical role in scientific communication by consolidating progress across a field. Recent advances in Large Language Models (LLMs) offer a promising solution…
MaCP: Minimal yet Mighty Adaptation via Hierarchical Cosine Projection
Yixian Shen, Qi Bi, Jia-Hong Huang +3
We present a new adaptation method MaCP, Minimal yet Mighty adaptive Cosine Projection, that achieves exceptional performance while requiring minimal parameters and memory for fine…
SSH: Sparse Spectrum Adaptation via Discrete Hartley Transformation
Yixian Shen, Qi Bi, Jia-Hong Huang +3
Low-rank adaptation (LoRA) has been demonstrated effective in reducing the trainable parameter number when fine-tuning a large foundation model (LLM). However, it still encounters…
Gradient Weight-normalized Low-rank Projection for Efficient LLM Training
Jia-Hong Huang, Yixian Shen, Hongyi Zhu +2
Large Language Models (LLMs) have shown remarkable performance across various tasks, but the escalating demands on computational resources pose significant challenges, particularly…
Image2Text2Image: A Novel Framework for Label-Free Evaluation of Image-to-Text Generation with Text-to-Image Diffusion Models
Jia-Hong Huang, Hongyi Zhu, Yixian Shen +2
Evaluating the quality of automatically generated image descriptions is a complex task that requires metrics capturing various dimensions, such as grammaticality, coverage, accurac…
MaCP: Minimal yet Mighty Adaptation via Hierarchical Cosine Projection
Yixian Shen, Qi Bi, Jia-Hong Huang +3
We present a new adaptation method MaCP, Minimal yet Mighty adaptive Cosine Projection, that achieves exceptional performance while requiring minimal parameters and memory for fine…
A Novel Evaluation Framework for Image2Text Generation
Jia-Hong Huang, Hongyi Zhu, Yixian Shen +3
Evaluating the quality of automatically generated image descriptions is challenging, requiring metrics that capture various aspects such as grammaticality, coverage, correctness, a…
Optimizing Numerical Estimation and Operational Efficiency in the Legal Domain through Large Language Models
Jia-Hong Huang, Chao-Chun Yang, Yixian Shen +2
The legal landscape encompasses a wide array of lawsuit types, presenting lawyers with challenges in delivering timely and accurate information to clients, particularly concerning…