6 citations · 8 across the 14 of their papers we have counts for
15 papers
P-SAM: SAM with Perceptual Parallel Prompt for Few-Shot Strip Steel Surface Defect Segmentation
Qian Xu, Hang Xiong, Anpeng Wang +3
Few-shot semantic segmentation (FSS) of strip steel surface defects (SD) has posed significant challenges distinct from natural scenes. Unlike natural images, SD task exhib…
MIMONet: Multi-scale Input and Multi-scale Output Network for Salient Object Detection
Zhaojian Yao, Wei Gao, Tiesong Zhao +2
The existing methods for saliency detection task focus on the application of multi-level features, aiming to take advantage of the respective strengths of high- and low-level featu…
RSONet: Region-guided Selective Optimization Network for RGB-T Salient Object Detection
Bin Wan, Runmin Cong, Xiaofei Zhou +3
This paper focuses on the inconsistency in salient regions between RGB and thermal images. To address this issue, we propose the Region-guided Selective Optimization Network for RG…
Towards Ancient Plant Seed Classification: A Benchmark Dataset and Baseline Model
Rui Xing, Runmin Cong, Yingying Wu +5
Understanding the dietary preferences of ancient societies and their evolution across periods and regions is crucial for revealing human-environment interactions. Seeds, as importa…
Enhanced Quality Aware-Scalable Underwater Image Compression
Linwei Zhu, Junhao Zhu, Xu Zhang +4
Underwater imaging plays a pivotal role in marine exploration and ecological monitoring. However, it faces significant challenges of limited transmission bandwidth and severe disto…
Beyond Global Scanning: Adaptive Visual State Space Modeling for Salient Object Detection in Optical Remote Sensing Images
Mengyu Ren, Yutong Li, Hua Li +2
Salient object detection (SOD) in optical remote sensing images (ORSIs) faces numerous challenges, including significant variations in target scales and low contrast between target…
UIS-Mamba: Exploring Mamba for Underwater Instance Segmentation via Dynamic Tree Scan and Hidden State Weaken
Runmin Cong, Zongji Yu, Hao Fang +2
Underwater Instance Segmentation (UIS) tasks are crucial for underwater complex scene detection. Mamba, as an emerging state space model with inherently linear complexity and globa…
From Sight to Insight: Unleashing Eye-Tracking in Weakly Supervised Video Salient Object Detection
Qi Qin, Runmin Cong, Gen Zhan +2
The eye-tracking video saliency prediction (VSP) task and video salient object detection (VSOD) task both focus on the most attractive objects in video and show the result in the f…
Proto-FG3D: Prototype-based Interpretable Fine-Grained 3D Shape Classification
Shuxian Ma, Zihao Dong, Runmin Cong +2
Deep learning-based multi-view coarse-grained 3D shape classification has achieved remarkable success over the past decade, leveraging the powerful feature learning capabilities of…
SEFE: Superficial and Essential Forgetting Eliminator for Multimodal Continual Instruction Tuning
Jinpeng Chen, Runmin Cong, Yuzhi Zhao +4
Multimodal Continual Instruction Tuning (MCIT) aims to enable Multimodal Large Language Models (MLLMs) to incrementally learn new tasks without catastrophic forgetting. In this pap…
Advancing Marine Research: UWSAM Framework and UIIS10K Dataset for Precise Underwater Instance Segmentation
Hua Li, Shijie Lian, Zhiyuan Li +5
With recent breakthroughs in large-scale modeling, the Segment Anything Model (SAM) has demonstrated significant potential in a variety of visual applications. However, due to the…
Unleashing Correlation and Continuity for Hyperspectral Reconstruction from RGB Images
Fuxiang Feng, Runmin Cong, Shoushui Wei +4
Reconstructing Hyperspectral Images (HSI) from RGB images can yield high spatial resolution HSI at a lower cost, demonstrating significant application potential. This paper reveals…
Strike a Balance in Continual Panoptic Segmentation
Jinpeng Chen, Runmin Cong, Yuxuan Luo +2
This study explores the emerging area of continual panoptic segmentation, highlighting three key balances. First, we introduce past-class backtrace distillation to balance the stab…
Diving into Underwater: Segment Anything Model Guided Underwater Salient Instance Segmentation and A Large-scale Dataset
Shijie Lian, Ziyi Zhang, Hua Li +4
With the breakthrough of large models, Segment Anything Model (SAM) and its extensions have been attempted to apply in diverse tasks of computer vision. Underwater salient instance…
Learning Hierarchical Color Guidance for Depth Map Super-Resolution
Runmin Cong, Ronghui Sheng, Hao Wu +5
Color information is the most commonly used prior knowledge for depth map super-resolution (DSR), which can provide high-frequency boundary guidance for detail restoration. However…