53 citations · 120 across the 20 of their papers we have counts for
21 papers
HeatTok: Enhancing Remote Sensing Image Understanding via Thermodiffusion-based Tokenization
Yingying Yan, Jiaqi Tang, Wei Wei +6
Current visual tokenizers in Multimodal Large Language Models (MLLMs) predominantly rely on patch-based partitioning, which causes severe semantic mixture and object fragmentation…
IQA-T1: Tool-based Visual Evidence Reasoning for Image Quality Assessment
Jinjian Wu, Jiaqi Tang, Wei Wei +5
Image Quality Assessment (IQA) in open-world environments remains challenging due to limited generalization and interpretability. Recent approaches based on multimodal large langua…
From Contrast to Consistency: Rethinking Event-based Continuous-Time Optical Flow Estimation
Rui Hu, Song Wu, Wen Yang +1
Estimating continuous optical flow is a fundamental yet challenging problem in dynamic visual perception. Event-based cameras, with microsecond latency and high dynamic range, capt…
Scaling Dense Event-Stream Pretraining from Visual Foundation Models
Zhiwen Chen, Junhui Hou, Zhiyu Zhu +2
Learning versatile, fine-grained representations from irregular event streams is pivotal yet nontrivial, primarily due to the heavy annotation that hinders scalability in dataset s…
Towards Syn-to-Real IQA: A Novel Perspective on Reshaping Synthetic Data Distributions
Aobo Li, Jinjian Wu, Yongxu Liu +2
Blind Image Quality Assessment (BIQA) has advanced significantly through deep learning, but the scarcity of large-scale labeled datasets remains a challenge. While synthetic data o…
Optimizing Multi-Modality Trackers via Significance-Regularized Tuning
Zhiwen Chen, Jinjian Wu, Zhiyu Zhu +3
This paper tackles the critical challenge of optimizing multi-modality trackers by effectively adapting pre-trained models for RGB data. Existing fine-tuning paradigms oscillate be…
Language-Guided Visual Perception Disentanglement for Image Quality Assessment and Conditional Image Generation
Zhichao Yang, Leida Li, Pengfei Chen +2
Contrastive vision-language models, such as CLIP, have demonstrated excellent zero-shot capability across semantic recognition tasks, mainly attributed to the training on a large-s…
An O(m+n)-Space Spatiotemporal Denoising Filter with Cache-Like Memories for Dynamic Vision Sensors
Qinghang Zhao, Jiaqi Wang, Yixi Ji +2
Dynamic vision sensor (DVS) is novel neuromorphic imaging device that generates asynchronous events. Despite the high temporal resolution and high dynamic range features, DVS is fa…
E-Motion: Future Motion Simulation via Event Sequence Diffusion
Song Wu, Zhiyu Zhu, Junhui Hou +2
Forecasting a typical object's future motion is a critical task for interpreting and interacting with dynamic environments in computer vision. Event-based sensors, which could capt…
Bridging the Synthetic-to-Authentic Gap: Distortion-Guided Unsupervised Domain Adaptation for Blind Image Quality Assessment
Aobo Li, Jinjian Wu, Yongxu Liu +1
The annotation of blind image quality assessment (BIQA) is labor-intensive and time-consuming, especially for authentic images. Training on synthetic data is expected to be benefic…
Modeling State Shifting via Local-Global Distillation for Event-Frame Gaze Tracking
Jiading Li, Zhiyu Zhu, Jinhui Hou +2
This paper tackles the problem of passive gaze estimation using both event and frame data. Considering the inherently different physiological structures, it is intractable to accur…
Fast Window-Based Event Denoising with Spatiotemporal Correlation Enhancement
Huachen Fang, Jinjian Wu, Qibin Hou +2
Previous deep learning-based event denoising methods mostly suffer from poor interpretability and difficulty in real-time processing due to their complex architecture designs. In t…
Scaling and Masking: A New Paradigm of Data Sampling for Image and Video Quality Assessment
Yongxu Liu, Yinghui Quan, Guoyao Xiao +2
Quality assessment of images and videos emphasizes both local details and global semantics, whereas general data sampling methods (e.g., resizing, cropping or grid-based fragment)…
Self-supervised Learning of LiDAR 3D Point Clouds via 2D-3D Neural Calibration
Yifan Zhang, Junhui Hou, Siyu Ren +3
This paper introduces a novel self-supervised learning framework for enhancing 3D perception in autonomous driving scenes. Specifically, our approach, namely NCLR, focuses on 2D-3D…
Segment Any Events via Weighted Adaptation of Pivotal Tokens
Zhiwen Chen, Zhiyu Zhu, Yifan Zhang +3
In this paper, we delve into the nuanced challenge of tailoring the Segment Anything Models (SAMs) for integration with event data, with the overarching objective of attaining robu…
Deep Posterior Distribution-based Embedding for Hyperspectral Image Super-resolution
Jinhui Hou, Zhiyu Zhu, Junhui Hou +3
In this paper, we investigate the problem of hyperspectral (HS) image spatial super-resolution via deep learning. Particularly, we focus on how to embed the high-dimensional spatia…
Robust Depth Completion with Uncertainty-Driven Loss Functions
Yufan Zhu, Weisheng Dong, Leida Li +3
Recovering a dense depth image from sparse LiDAR scans is a challenging task. Despite the popularity of color-guided methods for sparse-to-dense depth completion, they treated pixe…
Searching Efficient Model-guided Deep Network for Image Denoising
Qian Ning, Weisheng Dong, Xin Li +3
Neural architecture search (NAS) has recently reshaped our understanding on various vision tasks. Similar to the success of NAS in high-level vision tasks, it is possible to find a…
Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging
Tao Huang, Weisheng Dong, Xin Yuan +2
In coded aperture snapshot spectral imaging (CASSI) system, the real-world hyperspectral image (HSI) can be reconstructed from the captured compressive image in a snapshot. Model-b…
MetaIQA: Deep Meta-learning for No-Reference Image Quality Assessment
Hancheng Zhu, Leida Li, Jinjian Wu +2
Recently, increasing interest has been drawn in exploiting deep convolutional neural networks (DCNNs) for no-reference image quality assessment (NR-IQA). Despite of the notable suc…
Feature-Fused SSD: Fast Detection for Small Objects
Guimei Cao, Xuemei Xie, Wenzhe Yang +3
Small objects detection is a challenging task in computer vision due to its limited resolution and information. In order to solve this problem, the majority of existing methods sac…