1 citations · 1 across the 9 of their papers we have counts for
10 papers
KaoLRM: Repurposing Pre-trained Large Reconstruction Models for Parametric 3D Face Reconstruction
Qingtian Zhu, Xu Cao, Zhixiang Wang +2
We propose KaoLRM to re-target the learned prior of the Large Reconstruction Model (LRM) for parametric 3D face reconstruction from single-view images. Parametric 3D Morphable Mode…
The Geometry of Persona: Disentangling Personality from Reasoning in Large Language Models
Zhixiang Wang
Background: The deployment of personalized Large Language Models (LLMs) is currently constrained by the stability-plasticity dilemma. Prevailing alignment methods, such as Supervis…
Stream-DiffVSR: Low-Latency Streamable Video Super-Resolution via Auto-Regressive Diffusion
Hau-Shiang Shiu, Chin-Yang Lin, Zhixiang Wang +4
Diffusion-based video super-resolution (VSR) methods deliver strong perceptual quality but are often unsuitable for latency-sensitive scenarios due to reliance on future frames and…
Benchmarking MLLM-based Web Understanding: Reasoning, Robustness and Safety
Junliang Liu, Jingyu Xiao, Wenxin Tang +5
Multimodal large language models (MLLMs) are increasingly deployed as the core reasoning engine for web-facing systems, powering GUI agents and front-end automation that must inter…
Sekai: A Video Dataset towards World Exploration
Zhen Li, Chuanhao Li, Xiaofeng Mao +17
Video generation techniques have made remarkable progress, promising to be the foundation of interactive world exploration. However, existing video generation datasets are not well…
Beyond Demonstrations: Dynamic Vector Construction from Latent Representations
Wang Cai, Hsiu-Yuan Huang, Zhixiang Wang +1
In-Context derived Vector (ICV) methods extract task-relevant representations from large language models (LLMs) and reinject them during inference, achieving comparable performance…
Optimal Brain Iterative Merging: Mitigating Interference in LLM Merging
Zhixiang Wang, Zhenyu Mao, Yixuan Qiao +2
Large Language Models (LLMs) have demonstrated impressive capabilities, but their high computational costs pose challenges for customization. Model merging offers a cost-effective…
SCOI: Syntax-augmented Coverage-based In-context Example Selection for Machine Translation
Chenming Tang, Zhixiang Wang, Yunfang Wu
In-context learning (ICL) greatly improves the performance of large language models (LLMs) on various down-stream tasks, where the improvement highly depends on the quality of demo…
Large Language Models Might Not Care What You Are Saying: Prompt Format Beats Descriptions
Chenming Tang, Zhixiang Wang, Hao Sun +1
With the help of in-context learning (ICL), large language models (LLMs) have achieved impressive performance across various tasks. However, the function of descriptive instruction…
Going Beyond Word Matching: Syntax Improves In-context Example Selection for Machine Translation
Chenming Tang, Zhixiang Wang, Yunfang Wu
In-context learning (ICL) is the trending prompting strategy in the era of large language models (LLMs), where a few examples are demonstrated to evoke LLMs' power for a given task…