23 citations · 55 across the 14 of their papers we have counts for
14 papers
AdaSurvMamba: Dynamic Fusion and Semantic Scanning for Multimodal Survival Analysis
Jialong Zhong, Tingwei Liu, Baokun Yue +7
Multimodal survival analysis utilizing whole slide images (WSIs) and genomic profiles is fundamental for cancer prognosis. Recently, state-space models like Mamba have emerged as p…
Selective Noise Suppression and Discriminative Mutual Interaction for Robust Audio-Visual Segmentation
Kai Peng, Yunzhe Shen, Miao Zhang +6
The ability to capture and segment sounding objects in dynamic visual scenes is crucial for the development of Audio-Visual Segmentation (AVS) tasks. While significant progress has…
Utilizing Earth Foundation Models to Enhance the Simulation Performance of Hydrological Models with AlphaEarth Embeddings
Pengfei Qu, Wenyu Ouyang, Chi Zhang +6
Predicting river flow in places without streamflow records is challenging because basins respond differently to climate, terrain, vegetation, and soils. Traditional basin attribute…
SAM3-I: Segment Anything with Instructions
Jingjing Li, Yue Feng, Yuchen Guo +10
Segment Anything Model 3 (SAM3) advances open-vocabulary segmentation through promptable concept segmentation, enabling users to segment all instances associated with a given conce…
Frequency-Domain Decomposition and Recomposition for Robust Audio-Visual Segmentation
Yunzhe Shen, Kai Peng, Leiye Liu +6
Audio-visual segmentation (AVS) plays a critical role in multimodal machine learning by effectively integrating audio and visual cues to precisely segment objects or regions within…
P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation
Zhenyan Yao, Miao Zhang, Lanhu Wu +4
Perturbation with diverse unlabeled data has proven beneficial for semi-supervised medical image segmentation (SSMIS). While many works have successfully used various perturbation…
DefMamba: Deformable Visual State Space Model
Leiye Liu, Miao Zhang, Jihao Yin +4
Recently, state space models (SSM), particularly Mamba, have attracted significant attention from scholars due to their ability to effectively balance computational efficiency and…
CNN-Transformer Rectified Collaborative Learning for Medical Image Segmentation
Lanhu Wu, Miao Zhang, Yongri Piao +4
Automatic and precise medical image segmentation (MIS) is of vital importance for clinical diagnosis and analysis. Current MIS methods mainly rely on the convolutional neural netwo…
CriDiff: Criss-cross Injection Diffusion Framework via Generative Pre-train for Prostate Segmentation
Tingwei Liu, Miao Zhang, Leiye Liu +4
Recently, the Diffusion Probabilistic Model (DPM)-based methods have achieved substantial success in the field of medical image segmentation. However, most of these methods fail to…
Learning Multi-modal Information for Robust Light Field Depth Estimation
Yongri Piao, Xinxin Ji, Miao Zhang +1
Light field data has been demonstrated to facilitate the depth estimation task. Most learning-based methods estimate the depth infor-mation from EPI or sub-aperture images, while l…
Dynamic Fusion Network For Light Field Depth Estimation
Yongri Piao, Yukun Zhang, Miao Zhang +1
Focus based methods have shown promising results for the task of depth estimation. However, most existing focus based depth estimation approaches depend on maximal sharpness of the…
DUT-LFSaliency: Versatile Dataset and Light Field-to-RGB Saliency Detection
Yongri Piao, Zhengkun Rong, Shuang Xu +2
Light field data exhibit favorable characteristics conducive to saliency detection. The success of learning-based light field saliency detection is heavily dependent on how a compr…
Accurate RGB-D Salient Object Detection via Collaborative Learning
Wei Ji, Jingjing Li, Miao Zhang +2
Benefiting from the spatial cues embedded in depth images, recent progress on RGB-D saliency detection shows impressive ability on some challenge scenarios. However, there are stil…
RGB-D Salient Object Detection with Cross-Modality Modulation and Selection
Chongyi Li, Runmin Cong, Yongri Piao +2
We present an effective method to progressively integrate and refine the cross-modality complementarities for RGB-D salient object detection (SOD). The proposed network mainly solv…