4 citations · 11 across the 11 of their papers we have counts for
13 papers
SHarP: Saliency-based Pruning of Agent Harnesses
Xinyi Gao, Qiucheng Wu, Kaizhi Qian +3
Agent harnesses are systems that coordinate model calls, tool use, and task execution to help large language models complete complex tasks. To meet task requirements and address fa…
WorldAuditBench: Interactive 3D World Auditing with Multimodal Agents
Ziyan Jiang, Jingbo Yang, Jiabao Ji +5
As interactive 3D worlds are increasingly used to study intelligent behavior, it becomes important to develop efficient pipelines for identifying anomalies in these simulated envir…
VISUALSKILL: Multimodal Skills for Computer-Use Agents
Ziyan Jiang, Li An, Yujian Liu +5
Computer-use agents (CUAs) approach human-level performance on standardised benchmarks but still struggle on long-horizon tasks and unseen software. Existing skill libraries addres…
RetouchIQ: MLLM Agents for Instruction-Based Image Retouching with Generalist Reward
Qiucheng Wu, Jing Shi, Simon Jenni +4
Recent advances in multimodal large language models (MLLMs) have shown great potential for extending vision-language reasoning to professional tool-based image editing, enabling in…
VividCam: Learning Unconventional Camera Motions from Virtual Synthetic Videos
Qiucheng Wu, Handong Zhao, Zhixin Shu +3
Although recent video generative models are getting more capable of following external camera controls, imposed by either text descriptions or camera trajectories, they still strug…
Rethinking the Text-Vision Reasoning Imbalance in MLLMs through the Lens of Training Recipes
Guanyu Yao, Qiucheng Wu, Yang Zhang +3
Multimodal large language models (MLLMs) have demonstrated strong capabilities on vision-and-language tasks. However, recent findings reveal an imbalance in their reasoning capabil…
A Hierarchical Probabilistic Framework for Incremental Knowledge Tracing in Classroom Settings
Xinyi Gao, Qiucheng Wu, Yang Zhang +4
Knowledge tracing (KT) aims to estimate a student's evolving knowledge state and predict their performance on new exercises based on performance history. Many realistic classroom s…
VSP: Assessing the dual challenges of perception and reasoning in spatial planning tasks for VLMs
Qiucheng Wu, Handong Zhao, Michael Saxon +4
Vision language models (VLMs) are an exciting emerging class of language models (LMs) that have merged classic LM capabilities with those of image processing systems. However, the…
Harnessing the Spatial-Temporal Attention of Diffusion Models for High-Fidelity Text-to-Image Synthesis
Qiucheng Wu, Yujian Liu, Handong Zhao +4
Diffusion-based models have achieved state-of-the-art performance on text-to-image synthesis tasks. However, one critical limitation of these models is the low fidelity of generate…
Uncovering the Disentanglement Capability in Text-to-Image Diffusion Models
Qiucheng Wu, Yujian Liu, Handong Zhao +6
Generative models have been widely studied in computer vision. Recently, diffusion models have drawn substantial attention due to the high quality of their generated images. A key…
Data-Model-Circuit Tri-Design for Ultra-Light Video Intelligence on Edge Devices
Yimeng Zhang, Akshay Karkal Kamath, Qiucheng Wu +6
In this paper, we propose a data-model-hardware tri-design framework for high-throughput, low-cost, and high-accuracy multi-object tracking (MOT) on High-Definition (HD) video stre…
Learning Action Translator for Meta Reinforcement Learning on Sparse-Reward Tasks
Yijie Guo, Qiucheng Wu, Honglak Lee
Meta reinforcement learning (meta-RL) aims to learn a policy solving a set of training tasks simultaneously and quickly adapting to new tasks. It requires massive amounts of data d…
Grasping the Arrow of Time from the Singularity: Decoding Micromotion in Low-dimensional Latent Spaces from StyleGAN
Qiucheng Wu, Yifan Jiang, Junru Wu +5
The disentanglement of StyleGAN latent space has paved the way for realistic and controllable image editing, but does StyleGAN know anything about temporal motion, as it was only t…