2 citations · 3 across the 14 of their papers we have counts for
15 papers
DK-VLM: Geometry as Position for Spatial Reasoning in Vision-Language Models
Han Su, Meng Tian, Tianyu Huang +7
Vision-language models still struggle to understand 3D spatial relations across views and update spatial judgments as the observer moves. A common approach fuses 3D reconstruction…
GS-RealBlur: A Flexible Data Acquisition Framework for Real-World Image Deblurring
Mingyang Chen, Zhilu Zhang, Honglei Xu +3
High-quality, large-scale paired data is essential for training learning-based image deblurring models. However, synthetic blurry images generally lack realism, while real-world ca…
Illuminating Unified Multimodal Model for Free-form Interleaved Text-Image Generation
Chonghuinan Wang, Zhikai Chen, Chunwei Wang +9
The advancement of generative AI models capable of producing text and image marks a critical step forward in the realm of multimodal intelligence, particularly for tasks involving…
CREval: An Automated Interpretable Evaluation for Creative Image Manipulation under Complex Instructions
Chonghuinan Wang, Zihan Chen, Yuxiang Wei +5
Instruction-based multimodal image manipulation has recently made rapid progress. However, existing evaluation methods lack a systematic and human-aligned framework for assessing m…
MiM-DiT: MoE in MoE with Diffusion Transformers for All-in-One Image Restoration
Lingshun Kong, Jiawei Zhang, Zhengpeng Duan +6
All-in-one image restoration is challenging because different degradation types, such as haze, blur, noise, and low-light, impose diverse requirements on restoration strategies, ma…
S2AM3D: Scale-controllable Part Segmentation of 3D Point Clouds
Han Su, Tianyu Huang, Zichen Wan +2
Part-level point cloud segmentation has recently attracted significant attention in 3D computer vision. Nevertheless, existing research is constrained by two major challenges: nati…
SelfHVD: Self-Supervised Handheld Video Deblurring
Honglei Xu, Zhilu Zhang, Junjie Fan +2
Shooting video with handheld shooting devices often results in blurry frames due to shaking hands and other instability factors. Although previous video deblurring methods have ach…
Image Demoiréing Using Dual Camera Fusion on Mobile Phones
Yanting Mei, Zhilu Zhang, Xiaohe Wu +1
When shooting electronic screens, moiré patterns usually appear in captured images, which seriously affects the image quality. Existing image demoiréing methods face great challeng…
Pseudo-Label Guided Real-World Image De-weathering: A Learning Framework with Imperfect Supervision
Heming Xu, Xiaohui Liu, Zhilu Zhang +3
Real-world image de-weathering aims at removingvarious undesirable weather-related artifacts, e.g., rain, snow,and fog. To this end, acquiring ideal training pairs is crucial.Exist…
Generative Inbetweening through Frame-wise Conditions-Driven Video Generation
Tianyi Zhu, Dongwei Ren, Qilong Wang +2
Generative inbetweening aims to generate intermediate frame sequences by utilizing two key frames as input. Although remarkable progress has been made in video generation models, g…
Seeing Beyond Views: Multi-View Driving Scene Video Generation with Holistic Attention
Hannan Lu, Xiaohe Wu, Shudong Wang +5
Generating multi-view videos for autonomous driving training has recently gained much attention, with the challenge of addressing both cross-view and cross-frame consistency. Exist…
Reblurring-Guided Single Image Defocus Deblurring: A Learning Framework with Misaligned Training Pairs
Dongwei Ren, Xinya Shu, Yu Li +3
For single image defocus deblurring, acquiring well-aligned training pairs (or training triplets), i.e., a defocus blurry image, an all-in-focus sharp image (and a defocus blur map…
MC: Multi-concept Guidance for Customized Multi-concept Generation
Jiaxiu Jiang, Yabo Zhang, Kailai Feng +5
Customized text-to-image generation, which synthesizes images based on user-specified concepts, has made significant progress in handling individual concepts. However, when extende…
Learning with Noisy Labels Using Collaborative Sample Selection and Contrastive Semi-Supervised Learning
Qing Miao, Xiaohe Wu, Chao Xu +4
Learning with noisy labels (LNL) has been extensively studied, with existing approaches typically following a framework that alternates between clean sample selection and semi-supe…
Learning Real-World Image De-Weathering with Imperfect Supervision
Xiaohui Liu, Zhilu Zhang, Xiaohe Wu +4
Real-world image de-weathering aims at removing various undesirable weather-related artifacts. Owing to the impossibility of capturing image pairs concurrently, existing real-world…