8 citations · 14 across the 20 of their papers we have counts for
17 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…
Goal2Pixel: Grounding Goals to Pixels for Vision-Language Navigation
Muyi Bao, Yuxin Cai, Hang Xu +7
Vision-language models (VLMs) have become a common foundation for vision-and-language navigation in continuous environments (VLN-CE). Yet most VLM-based methods cast navigation as…
Evolving Contextual Safety in Multi-Modal Large Language Models via Inference-Time Self-Reflective Memory
Ce Zhang, Jinxi He, Junyi He +2
Multi-modal Large Language Models (MLLMs) have achieved remarkable performance across a wide range of visual reasoning tasks, yet their vulnerability to safety risks remains a pres…
pySpatial: Generating 3D Visual Programs for Zero-Shot Spatial Reasoning
Zhanpeng Luo, Ce Zhang, Silong Yong +6
Multi-modal Large Language Models (MLLMs) have demonstrated strong capabilities in general-purpose perception and reasoning, but they still struggle with tasks that require spatial…
ONLY: One-Layer Intervention Sufficiently Mitigates Hallucinations in Large Vision-Language Models
Zifu Wan, Ce Zhang, Silong Yong +6
Recent Large Vision-Language Models (LVLMs) have introduced a new paradigm for understanding and reasoning about image input through textual responses. Although they have achieved…
InstructPart: Task-Oriented Part Segmentation with Instruction Reasoning
Zifu Wan, Yaqi Xie, Ce Zhang +5
Large multimodal foundation models, particularly in the domains of language and vision, have significantly advanced various tasks, including robotics, autonomous driving, informati…
Spectral-Aware Global Fusion for RGB-Thermal Semantic Segmentation
Ce Zhang, Zifu Wan, Simon Stepputtis +2
Semantic segmentation relying solely on RGB data often struggles in challenging conditions such as low illumination and obscured views, limiting its reliability in critical applica…
VScan: Rethinking Visual Token Reduction for Efficient Large Vision-Language Models
Ce Zhang, Kaixin Ma, Tianqing Fang +5
Recent Large Vision-Language Models (LVLMs) have advanced multi-modal understanding by incorporating finer-grained visual perception and encoding. However, such methods incur signi…
OMG: Opacity Matters in Material Modeling with Gaussian Splatting
Silong Yong, Venkata Nagarjun Pudureddiyur Manivannan, Bernhard Kerbl +4
Decomposing geometry, materials and lighting from a set of images, namely inverse rendering, has been a long-standing problem in computer vision and graphics. Recent advances in ne…
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…
HiMemFormer: Hierarchical Memory-Aware Transformer for Multi-Agent Action Anticipation
Zirui Wang, Xinran Zhao, Simon Stepputtis +4
Understanding and predicting human actions has been a long-standing challenge and is a crucial measure of perception in robotics AI. While significant progress has been made in ant…
GL-NeRF: Gauss-Laguerre Quadrature Enables Training-Free NeRF Acceleration
Silong Yong, Yaqi Xie, Simon Stepputtis +1
Volume rendering in neural radiance fields is inherently time-consuming due to the large number of MLP calls on the points sampled per ray. Previous works would address this issue…
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…
Sigma: Siamese Mamba Network for Multi-Modal Semantic Segmentation
Zifu Wan, Pingping Zhang, Yuhao Wang +4
Multi-modal semantic segmentation significantly enhances AI agents' perception and scene understanding, especially under adverse conditions like low-light or overexposed environmen…
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…
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…