82 citations · 265 across the 36 of their papers we have counts for
39 papers · 1 filter
Single Image to Textured 3D Object Generation in Frequency Domain: From Theory to Pipeline
Qisen Wang, Yifan Zhao, Jia Li
Single-view 3D reconstruction, also known as image-to-3D, is a persistently challenging task due to the extreme lack of information. Recently, diffusion models pre-trained on large…
Adapting Dense Vision-Language Relationships for Multi-label Classification with Partial Label
Cheng Chen, Yifan Zhao, Jia Li
Learning multi-label image classification with incomplete annotations is a challenging task that has been widely studied for its superior trade-off between high efficiency and less…
The First EgoCross Challenge at EgoVis 2026: Cross-Domain Egocentric Video Question Answering
Yuqian Fu, Tianwen Qian, Yanjun Li +30
EgoCross is a cross-domain egocentric video question answering benchmark designed to evaluate whether multimodal large language models can generalize beyond common daily-life scena…
To Blend In, First Decouple: Rethinking Camouflage Image Generation via Context-Decoupled Representations
Wenzhuang Wang, Yifan Zhao, Mingcan Ma +4
Camouflage image generation (CIG) focuses on generating visually concealed objects that seamlessly blend into their backgrounds. Existing methods typically follow either background…
The Right Inference Strategy Is All You Need: Nearly Training-Free Domain-Wise Inference for EgoCross Challenge
Leyi Wu, Yifan Zhao, Jinjie Zhang +2
EgoCross evaluates multimodal large language models on egocentric video question answering under substantial domain shift, where test videos come from surgery, industrial assembly,…
RoboStressBench: Benchmarking VLM Robustness to Physical Visual Stress in Embodied Scenes
Leyi Wu, Yifan Zhao, Jinjie Zhang +11
Vision-Language Models (VLMs) have shown strong visual understanding and are increasingly deployed in embodied AI systems, where reliable perception under real conditions is essent…
Envisioning Beyond the Few: Disentangled Semantics and Primitives for Few-Shot Atypical Layout-to-Image Generation
Nan Bao, Yifan Zhao, Wenzhuang Wang +1
The layout-to-image (L2I) task enables fine-grained control over image generation via object categories and spatial layouts. However, existing L2I methods yield fragmented and dist…
WorldTree: Towards 4D Dynamic Worlds from Monocular Video using Tree-Chains
Qisen Wang, Yifan Zhao, Jia Li
Dynamic reconstruction has achieved remarkable progress, but there remain challenges in monocular input for more practical applications. The prevailing works attempt to construct e…
Seeing through Light and Darkness: Sensor-Physics Grounded Deblurring HDR NeRF from Single-Exposure Images and Events
Yunshan Qi, Lin Zhu, Nan Bao +2
Novel view synthesis from low dynamic range (LDR) blurry images, which are common in the wild, struggles to recover high dynamic range (HDR) and sharp 3D representations in extreme…
ChronosObserver: Taming 4D World with Hyperspace Diffusion Sampling
Qisen Wang, Yifan Zhao, Peisen Shen +2
Although prevailing camera-controlled video generation models can produce cinematic results, lifting them directly to the generation of 3D-consistent and high-fidelity time-synchro…
PolarGS: Polarimetric Cues for Ambiguity-Free Gaussian Splatting with Accurate Geometry Recovery
Bo Guo, Sijia Wen, Yifan Zhao +2
Recent advances in surface reconstruction for 3D Gaussian Splatting (3DGS) have enabled remarkable geometric accuracy. However, their performance degrades in photometrically ambigu…
Free Lunch to Meet the Gap: Intermediate Domain Reconstruction for Cross-Domain Few-Shot Learning
Tong Zhang, Yifan Zhao, Liangyu Wang +1
Cross-Domain Few-Shot Learning (CDFSL) endeavors to transfer generalized knowledge from the source domain to target domains using only a minimal amount of training data, which face…
Re-coding for Uncertainties: Edge-awareness Semantic Concordance for Resilient Event-RGB Segmentation
Nan Bao, Yifan Zhao, Lin Zhu +1
Semantic segmentation has achieved great success in ideal conditions. However, when facing extreme conditions (e.g., insufficient light, fierce camera motion), most existing method…
Diffusion-Classifier Synergy: Reward-Aligned Learning via Mutual Boosting Loop for FSCIL
Ruitao Wu, Yifan Zhao, Guangyao Chen +1
Few-Shot Class-Incremental Learning (FSCIL) challenges models to sequentially learn new classes from minimal examples without forgetting prior knowledge, a task complicated by the…
FICGen: Frequency-Inspired Contextual Disentanglement for Layout-driven Degraded Image Generation
Wenzhuang Wang, Yifan Zhao, Mingcan Ma +4
Layout-to-image (L2I) generation has exhibited promising results in natural domains, but suffers from limited generative fidelity and weak alignment with user-provided layouts when…
Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement
Ruitao Wu, Yifan Zhao, Jia Li
Class-Incremental Semantic Segmentation (CISS) requires continuous learning of newly introduced classes while retaining knowledge of past classes. By abstracting mainstream methods…
When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network
Dong Xiao, Guangyao Chen, Peixi Peng +4
Anomaly detection is essential for the safety and reliability of autonomous driving systems. Current methods often focus on detection accuracy but neglect response time, which is c…
Provoking Multi-modal Few-Shot LVLM via Exploration-Exploitation In-Context Learning
Cheng Chen, Yunpeng Zhai, Yifan Zhao +3
In-context learning (ICL), a predominant trend in instruction learning, aims at enhancing the performance of large language models by providing clear task guidance and examples, im…
Language-Inspired Relation Transfer for Few-shot Class-Incremental Learning
Yifan Zhao, Jia Li, Zeyin Song +1
Depicting novel classes with language descriptions by observing few-shot samples is inherent in human-learning systems. This lifelong learning capability helps to distinguish new k…
How to Use Diffusion Priors under Sparse Views?
Qisen Wang, Yifan Zhao, Jiawei Ma +1
Novel view synthesis under sparse views has been a long-term important challenge in 3D reconstruction. Existing works mainly rely on introducing external semantic or depth priors t…
ENeRF: Efficient Event-Enhanced Neural Radiance Fields from Blurry Images
Yunshan Qi, Jia Li, Yifan Zhao +2
Neural Radiance Fields (NeRF) achieves impressive novel view rendering performance by learning implicit 3D representation from sparse view images. However, it is difficult to recon…
Deblurring Neural Radiance Fields with Event-driven Bundle Adjustment
Yunshan Qi, Lin Zhu, Yifan Zhao +2
Neural Radiance Fields (NeRF) achieves impressive 3D representation learning and novel view synthesis results with high-quality multi-view images as input. However, motion blur in…
Sensitivity Decouple Learning for Image Compression Artifacts Reduction
Li Ma, Yifan Zhao, Peixi Peng +1
With the benefit of deep learning techniques, recent researches have made significant progress in image compression artifacts reduction. Despite their improved performances, prevai…
SpikeNeRF: Learning Neural Radiance Fields from Continuous Spike Stream
Lin Zhu, Kangmin Jia, Yifan Zhao +3
Spike cameras, leveraging spike-based integration sampling and high temporal resolution, offer distinct advantages over standard cameras. However, existing approaches reliant on sp…