2 citations · 7 across the 13 of their papers we have counts for
14 papers
StreamScout: Learning When to Look Deeper for Streaming Video Understanding
Ce Zhang, Jing Bi, Jinxi He +9
Streaming video understanding requires answering questions that arrive at arbitrary moments over an unbounded video stream. Existing systems primarily focus on what to retain in a…
LENS: Adaptive Spatio-Temporal Zooming for Keyframe Sampling in Long-Form Videos
Ce Zhang, Jinxi He, Katia Sycara +1
Despite rapid progress in Multi-modal Large Language Models (MLLMs), understanding long-form videos is still bottlenecked by limited context windows. While recent keyframe sampling…
Self-Correcting Decoding with Generative Feedback for Mitigating Hallucinations in Large Vision-Language Models
Ce Zhang, Zifu Wan, Zhehan Kan +7
While recent Large Vision-Language Models (LVLMs) have shown remarkable performance in multi-modal tasks, they are prone to generating hallucinatory text responses that do not alig…
Dual Prototype Evolving for Test-Time Generalization of Vision-Language Models
Ce Zhang, Simon Stepputtis, Katia Sycara +1
Test-time adaptation, which enables models to generalize to diverse data with unlabeled test samples, holds significant value in real-world scenarios. Recently, researchers have ap…
HiKER-SGG: Hierarchical Knowledge Enhanced Robust Scene Graph Generation
Ce Zhang, Simon Stepputtis, Joseph Campbell +2
Being able to understand visual scenes is a precursor for many downstream tasks, including autonomous driving, robotics, and other vision-based approaches. A common approach enabli…
Test-time Distribution Learning Adapter for Cross-modal Visual Reasoning
Yi Zhang, Ce Zhang
Vision-Language Pre-Trained (VLP) models, such as CLIP, have demonstrated remarkable effectiveness in learning generic visual representations. Several approaches aim to efficiently…
Enhancing Vision-Language Few-Shot Adaptation with Negative Learning
Ce Zhang, Simon Stepputtis, Katia Sycara +1
Large-scale pre-trained Vision-Language Models (VLMs) have exhibited impressive zero-shot performance and transferability, allowing them to adapt to downstream tasks in a data-effi…
Concept-Guided Prompt Learning for Generalization in Vision-Language Models
Yi Zhang, Ce Zhang, Ke Yu +2
Contrastive Language-Image Pretraining (CLIP) model has exhibited remarkable efficacy in establishing cross-modal connections between texts and images, yielding impressive performa…
Learning to Adapt CLIP for Few-Shot Monocular Depth Estimation
Xueting Hu, Ce Zhang, Yi Zhang +3
Pre-trained Vision-Language Models (VLMs), such as CLIP, have shown enhanced performance across a range of tasks that involve the integration of visual and linguistic modalities. W…
BDC-Adapter: Brownian Distance Covariance for Better Vision-Language Reasoning
Yi Zhang, Ce Zhang, Zihan Liao +2
Large-scale pre-trained Vision-Language Models (VLMs), such as CLIP and ALIGN, have introduced a new paradigm for learning transferable visual representations. Recently, there has…
Cross-Modal Concept Learning and Inference for Vision-Language Models
Yi Zhang, Ce Zhang, Yushun Tang +1
Large-scale pre-trained Vision-Language Models (VLMs), such as CLIP, establish the correlation between texts and images, achieving remarkable success on various downstream tasks wi…
Critical Sampling for Robust Evolution Operator Learning of Unknown Dynamical Systems
Ce Zhang, Kailiang Wu, Zhihai He
Given an unknown dynamical system, what is the minimum number of samples needed for effective learning of its governing laws and accurate prediction of its future evolution behavio…
Self-Correctable and Adaptable Inference for Generalizable Human Pose Estimation
Zhehan Kan, Shuoshuo Chen, Ce Zhang +2
A central challenge in human pose estimation, as well as in many other machine learning and prediction tasks, is the generalization problem. The learned network does not have the c…
Neuro-Modulated Hebbian Learning for Fully Test-Time Adaptation
Yushun Tang, Ce Zhang, Heng Xu +5
Fully test-time adaptation aims to adapt the network model based on sequential analysis of input samples during the inference stage to address the cross-domain performance degradat…