2 citations · 7 across the 13 of their papers we have counts for
13 papers · 1 filter
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…
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…