40 citations · 69 across the 23 of their papers we have counts for
23 papers
MAGIC++: Efficient and Resilient Modality-Agnostic Semantic Segmentation via Hierarchical Modality Selection
Xu Zheng, Yuanhuiyi Lyu, Lutao Jiang +3
In this paper, we address the challenging modality-agnostic semantic segmentation (MaSS), aiming at centering the value of every modality at every feature granularity. Training wit…
EventDance++: Language-guided Unsupervised Source-free Cross-modal Adaptation for Event-based Object Recognition
Xu Zheng, Lin Wang
In this paper, we address the challenging problem of cross-modal (image-to-events) adaptation for event-based recognition without accessing any labeled source image data. This task…
Centering the Value of Every Modality: Towards Efficient and Resilient Modality-agnostic Semantic Segmentation
Xu Zheng, Yuanhuiyi Lyu, Jiazhou Zhou +1
Fusing an arbitrary number of modalities is vital for achieving robust multi-modal fusion of semantic segmentation yet remains less explored to date. Recent endeavors regard RGB mo…
GoodSAM++: Bridging Domain and Capacity Gaps via Segment Anything Model for Panoramic Semantic Segmentation
Weiming Zhang, Yexin Liu, Xu Zheng +1
This paper presents GoodSAM++, a novel framework utilizing the powerful zero-shot instance segmentation capability of SAM (i.e., teacher) to learn a compact panoramic semantic segm…
Learning Modality-agnostic Representation for Semantic Segmentation from Any Modalities
Xu Zheng, Yuanhuiyi Lyu, Lin Wang
Image modality is not perfect as it often fails in certain conditions, e.g., night and fast motion. This significantly limits the robustness and versatility of existing multi-modal…
EIT-1M: One Million EEG-Image-Text Pairs for Human Visual-textual Recognition and More
Xu Zheng, Ling Wang, Kanghao Chen +3
Recently, electroencephalography (EEG) signals have been actively incorporated to decode brain activity to visual or textual stimuli and achieve object recognition in multi-modal A…
OmniBind: Teach to Build Unequal-Scale Modality Interaction for Omni-Bind of All
Yuanhuiyi Lyu, Xu Zheng, Dahun Kim +1
Research on multi-modal learning dominantly aligns the modalities in a unified space at training, and only a single one is taken for prediction at inference. However, for a real ma…
360SFUDA++: Towards Source-free UDA for Panoramic Segmentation by Learning Reliable Category Prototypes
Xu Zheng, Pengyuan Zhou, Athanasios V. Vasilakos +1
In this paper, we address the challenging source-free unsupervised domain adaptation (SFUDA) for pinhole-to-panoramic semantic segmentation, given only a pinhole image pre-trained…
GoodSAM: Bridging Domain and Capacity Gaps via Segment Anything Model for Distortion-aware Panoramic Semantic Segmentation
Weiming Zhang, Yexin Liu, Xu Zheng +1
This paper tackles a novel yet challenging problem: how to transfer knowledge from the emerging Segment Anything Model (SAM) -- which reveals impressive zero-shot instance segmenta…
Semantics, Distortion, and Style Matter: Towards Source-free UDA for Panoramic Segmentation
Xu Zheng, Pengyuan Zhou, Athanasios V. Vasilakos +1
This paper addresses an interesting yet challenging problem -- source-free unsupervised domain adaptation (SFUDA) for pinhole-to-panoramic semantic segmentation -- given only a pin…
EventDance: Unsupervised Source-free Cross-modal Adaptation for Event-based Object Recognition
Xu Zheng, Lin Wang
In this paper, we make the first attempt at achieving the cross-modal (i.e., image-to-events) adaptation for event-based object recognition without accessing any labeled source ima…
ExACT: Language-guided Conceptual Reasoning and Uncertainty Estimation for Event-based Action Recognition and More
Jiazhou Zhou, Xu Zheng, Yuanhuiyi Lyu +1
Event cameras have recently been shown beneficial for practical vision tasks, such as action recognition, thanks to their high temporal resolution, power efficiency, and reduced pr…
UniBind: LLM-Augmented Unified and Balanced Representation Space to Bind Them All
Yuanhuiyi Lyu, Xu Zheng, Jiazhou Zhou +1
We present UniBind, a flexible and efficient approach that learns a unified representation space for seven diverse modalities -- images, text, audio, point cloud, thermal, video, a…
Image Anything: Towards Reasoning-coherent and Training-free Multi-modal Image Generation
Yuanhuiyi Lyu, Xu Zheng, Lin Wang
The multifaceted nature of human perception and comprehension indicates that, when we think, our body can naturally take any combination of senses, a.k.a., modalities and form a be…
Distilling Efficient Vision Transformers from CNNs for Semantic Segmentation
Xu Zheng, Yunhao Luo, Pengyuan Zhou +1
In this paper, we tackle a new problem: how to transfer knowledge from the pre-trained cumbersome yet well-performed CNN-based model to learn a compact Vision Transformer (ViT)-bas…
Chasing Day and Night: Towards Robust and Efficient All-Day Object Detection Guided by an Event Camera
Jiahang Cao, Xu Zheng, Yuanhuiyi Lyu +3
The ability to detect objects in all lighting (i.e., normal-, over-, and under-exposed) conditions is crucial for real-world applications, such as self-driving.Traditional RGB-base…
Look at the Neighbor: Distortion-aware Unsupervised Domain Adaptation for Panoramic Semantic Segmentation
Xu Zheng, Tianbo Pan, Yunhao Luo +1
Endeavors have been recently made to transfer knowledge from the labeled pinhole image domain to the unlabeled panoramic image domain via Unsupervised Domain Adaptation (UDA). The…
EventBind: Learning a Unified Representation to Bind Them All for Event-based Open-world Understanding
Jiazhou Zhou, Xu Zheng, Yuanhuiyi Lyu +1
In this paper, we propose EventBind, a novel and effective framework that unleashes the potential of vision-language models (VLMs) for event-based recognition to compensate for the…
A Good Student is Cooperative and Reliable: CNN-Transformer Collaborative Learning for Semantic Segmentation
Jinjing Zhu, Yunhao Luo, Xu Zheng +2
In this paper, we strive to answer the question "how to collaboratively learn convolutional neural network (CNN)-based and vision transformer (ViT)-based models by selecting and ex…
Both Style and Distortion Matter: Dual-Path Unsupervised Domain Adaptation for Panoramic Semantic Segmentation
Xu Zheng, Jinjing Zhu, Yexin Liu +3
The ability of scene understanding has sparked active research for panoramic image semantic segmentation. However, the performance is hampered by distortion of the equirectangular…
Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks
Xu Zheng, Yexin Liu, Yunfan Lu +5
Event cameras are bio-inspired sensors that capture the per-pixel intensity changes asynchronously and produce event streams encoding the time, pixel position, and polarity (sign)…
Transformer-CNN Cohort: Semi-supervised Semantic Segmentation by the Best of Both Students
Xu Zheng, Yunhao Luo, Chong Fu +2
The popular methods for semi-supervised semantic segmentation mostly adopt a unitary network model using convolutional neural networks (CNNs) and enforce consistency of the model's…
Priors in Deep Image Restoration and Enhancement: A Survey
Yunfan Lu, Yiqi Lin, Hao Wu +4
Image restoration and enhancement is a process of improving the image quality by removing degradations, such as noise, blur, and resolution degradation. Deep learning (DL) has rece…