11 papers · 1 filter
GrabVG: Graph-Attentive Binding for Visual Grounding in UAV Imagery
Chaowei Wang, Yan Di, Jingjun Sun +5
Visual grounding in Unmanned Aerial Vehicle (UAV) imagery aims to localize a target object in complex bird's-eye-view scenes according to a natural language description. However, t…
Surfsvr: 2D Surface Priors as 3D Geometric Regularizers for Sparse Voxel Reconstruction
Yan Di, Chengxi Li, Yaoxing Wang +7
Sparse voxel reconstruction offers an efficient representation for high-fidelity 3D modeling, yet its geometry is commonly optimized from local photometric evidence and discrete vi…
From Scene-Centric to Observer-Centric: Modeling Observer-Aware Relations for 3D Scene Graph Generation
Jingjun Sun, Chaowei Wang, Zhirui Liu +5
3D Scene Graph Generation (3DSGG) represents 3D scenes as structured object--relation--object graphs for spatial understanding. In observer-centric spatial perception, the same sce…
Boosting Segment Anything Model to Generalize Visually Non-Salient Scenarios
Guangqian Guo, Pengfei Chen, Yong Guo +3
Segment Anything Model (SAM), known for its remarkable zero-shot segmentation capabilities, has garnered significant attention in the community. Nevertheless, its performance is ch…
Towards Any-Quality Image Segmentation via Generative and Adaptive Latent Space Enhancement
Guangqian Guo, Aixi Ren, Yong Guo +6
Segment Anything Models (SAMs), known for their exceptional zero-shot segmentation performance, have garnered significant attention in the research community. Nevertheless, their p…
ReLayout: Integrating Relation Reasoning for Content-aware Layout Generation with Multi-modal Large Language Models
Jiaxu Tian, Xuehui Yu, Yaoxing Wang +3
Content-aware layout aims to arrange design elements appropriately on a given canvas to convey information effectively. Recently, the trend for this task has been to leverage large…
Segment Any-Quality Images with Generative Latent Space Enhancement
Guangqian Guo, Yong Guo, Xuehui Yu +3
Despite their success, Segment Anything Models (SAMs) experience significant performance drops on severely degraded, low-quality images, limiting their effectiveness in real-world…
Why mamba is effective? Exploit Linear Transformer-Mamba Network for Multi-Modality Image Fusion
Chenguang Zhu, Shan Gao, Huafeng Chen +5
Multi-modality image fusion aims to integrate the merits of images from different sources and render high-quality fusion images. However, existing feature extraction and fusion met…
Just a Hint: Point-Supervised Camouflaged Object Detection
Huafeng Chen, Dian Shao, Guangqian Guo +1
Camouflaged Object Detection (COD) demands models to expeditiously and accurately distinguish objects which conceal themselves seamlessly in the environment. Owing to the subtle di…
SAM-COD: SAM-guided Unified Framework for Weakly-Supervised Camouflaged Object Detection
Huafeng Chen, Pengxu Wei, Guangqian Guo +1
Most Camouflaged Object Detection (COD) methods heavily rely on mask annotations, which are time-consuming and labor-intensive to acquire. Existing weakly-supervised COD approaches…
Tracking without Label: Unsupervised Multiple Object Tracking via Contrastive Similarity Learning
Sha Meng, Dian Shao, Jiacheng Guo +1
Unsupervised learning is a challenging task due to the lack of labels. Multiple Object Tracking (MOT), which inevitably suffers from mutual object interference, occlusion, etc., is…