2 citations · 6 across the 28 of their papers we have counts for
28 papers
GFR-SAM: Training-Free Referring Camouflaged Object Segmentation via Cross-Image Prompting
Yilong Yang, Jianxin Tian, Shengchuan Zhang +1
Referring Camouflaged Object Detection (Ref-COD) requires segmenting hidden targets guided by reference cues. While supervised methods are annotation-heavy and training-free approa…
PAR3D: A Unified 3D-MLLM with Part-Aware Representation for Scene Understanding
Shaohui Dai, Yansong Qu, You Shen +2
Recent advances in 3D multimodal large language models (3D-MLLMs) have enabled unified solutions for 3D scene understanding tasks, including visual question answering, captioning,…
Active-SAOOD: Active Sparsely Annotated Oriented Object Detection in Remote Sensing Images
Yu Lin, Jianghang Lin, Kai Ye +2
Reducing the annotation cost of oriented object detection in remote sensing remains a major challenge. Recently, sparse annotation has gained attention for effectively reducing ann…
Discover, Segment, and Select: A Progressive Mechanism for Zero-shot Camouflaged Object Segmentation
Yilong Yang, Jianxin Tian, Shengchuan Zhang +1
Current zero-shot Camouflaged Object Segmentation methods typically employ a two-stage pipeline (discover-then-segment): using MLLMs to obtain visual prompts, followed by SAM segme…
FlashWorld: High-quality 3D Scene Generation within Seconds
Xinyang Li, Tengfei Wang, Zixiao Gu +3
We propose FlashWorld, a generative model that produces 3D scenes from a single image or text prompt in seconds, 10~100 faster than previous works while possessing superior…
SCOUT: Semi-supervised Camouflaged Object Detection by Utilizing Text and Adaptive Data Selection
Weiqi Yan, Lvhai Chen, Shengchuan Zhang +2
The difficulty of pixel-level annotation has significantly hindered the development of the Camouflaged Object Detection (COD) field. To save on annotation costs, previous works lev…
XSpecMesh: Quality-Preserving Auto-Regressive Mesh Generation Acceleration via Multi-Head Speculative Decoding
Dian Chen, Yansong Qu, Xinyang Li +2
Current auto-regressive models can generate high-quality, topologically precise meshes; however, they necessitate thousands-or even tens of thousands-of next-token predictions duri…
Generate Aligned Anomaly: Region-Guided Few-Shot Anomaly Image-Mask Pair Synthesis for Industrial Inspection
Yilin Lu, Jianghang Lin, Linhuang Xie +5
Anomaly inspection plays a vital role in industrial manufacturing, but the scarcity of anomaly samples significantly limits the effectiveness of existing methods in tasks such as l…
Inter2Former: Dynamic Hybrid Attention for Efficient High-Precision Interactive
You Huang, Lichao Chen, Jiayi Ji +3
Interactive segmentation (IS) improves annotation efficiency by segmenting target regions from user prompts, with widespread applications in real-world scenarios. Current approache…
UCOD-DPL: Unsupervised Camouflaged Object Detection via Dynamic Pseudo-label Learning
Weiqi Yan, Lvhai Chen, Huaijia Kou +3
Unsupervised Camoflaged Object Detection (UCOD) has gained attention since it doesn't need to rely on extensive pixel-level labels. Existing UCOD methods typically generate pseudo-…
What You Perceive Is What You Conceive: A Cognition-Inspired Framework for Open Vocabulary Image Segmentation
Jianghang Lin, Yue Hu, Jiangtao Shen +4
Open vocabulary image segmentation tackles the challenge of recognizing dynamically adjustable, predefined novel categories at inference time by leveraging vision-language alignmen…
Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation
Jianghang Lin, Yilin Lu, Yunhang Shen +4
Semi-Supervised Instance Segmentation (SSIS) involves classifying and grouping image pixels into distinct object instances using limited labeled data. This learning paradigm usuall…
SynergyAmodal: Deocclude Anything with Text Control
Xinyang Li, Chengjie Yi, Jiawei Lai +4
Image deocclusion (or amodal completion) aims to recover the invisible regions (\ie, shape and appearance) of occluded instances in images. Despite recent advances, the scarcity of…
Training-Free Hierarchical Scene Understanding for Gaussian Splatting with Superpoint Graphs
Shaohui Dai, Yansong Qu, Zheyan Li +3
Bridging natural language and 3D geometry is a crucial step toward flexible, language-driven scene understanding. While recent advances in 3D Gaussian Splatting (3DGS) have enabled…
BUFF: Bayesian Uncertainty Guided Diffusion Probabilistic Model for Single Image Super-Resolution
Zihao He, Shengchuan Zhang, Runze Hu +2
Super-resolution (SR) techniques are critical for enhancing image quality, particularly in scenarios where high-resolution imagery is essential yet limited by hardware constraints.…
Evolving High-Quality Rendering and Reconstruction in a Unified Framework with Contribution-Adaptive Regularization
You Shen, Zhipeng Zhang, Xinyang Li +4
Representing 3D scenes from multiview images is a core challenge in computer vision and graphics, which requires both precise rendering and accurate reconstruction. Recently, 3D Ga…
Drag Your Gaussian: Effective Drag-Based Editing with Score Distillation for 3D Gaussian Splatting
Yansong Qu, Dian Chen, Xinyang Li +4
Recent advancements in 3D scene editing have been propelled by the rapid development of generative models. Existing methods typically utilize generative models to perform text-guid…
Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection
Xin Chen, Liujuan Cao, Shengchuan Zhang +2
Due to the scarcity and unpredictable nature of defect samples, industrial anomaly detection (IAD) predominantly employs unsupervised learning. However, all unsupervised IAD method…
HRSAM: Efficient Interactive Segmentation in High-Resolution Images
You Huang, Wenbin Lai, Jiayi Ji +3
The Segment Anything Model (SAM) has advanced interactive segmentation but is limited by the high computational cost on high-resolution images. This requires downsampling to meet G…
Director3D: Real-world Camera Trajectory and 3D Scene Generation from Text
Xinyang Li, Zhangyu Lai, Linning Xu +5
Recent advancements in 3D generation have leveraged synthetic datasets with ground truth 3D assets and predefined cameras. However, the potential of adopting real-world datasets, w…
FocSAM: Delving Deeply into Focused Objects in Segmenting Anything
You Huang, Zongyu Lan, Liujuan Cao +4
The Segment Anything Model (SAM) marks a notable milestone in segmentation models, highlighted by its robust zero-shot capabilities and ability to handle diverse prompts. SAM follo…
Multi-Modal Prompt Learning on Blind Image Quality Assessment
Wensheng Pan, Timin Gao, Yan Zhang +10
Image Quality Assessment (IQA) models benefit significantly from semantic information, which allows them to treat different types of objects distinctly. Currently, leveraging seman…
Dual3D: Efficient and Consistent Text-to-3D Generation with Dual-mode Multi-view Latent Diffusion
Xinyang Li, Zhangyu Lai, Linning Xu +5
We present Dual3D, a novel text-to-3D generation framework that generates high-quality 3D assets from texts in only minute.The key component is a dual-mode multi-view latent di…
GOI: Find 3D Gaussians of Interest with an Optimizable Open-vocabulary Semantic-space Hyperplane
Yansong Qu, Shaohui Dai, Xinyang Li +4
3D open-vocabulary scene understanding, crucial for advancing augmented reality and robotic applications, involves interpreting and locating specific regions within a 3D space as d…