Search Results for author:
Found 14 papers, 4 papers with code
View-Centric Multi-Object Tracking with Homographic Matching in Moving UAV
In this paper, we address the challenge of multi-object tracking (MOT) in moving Unmanned Aerial Vehicle (UAV) scenarios, where irregular flight trajectories, such as hovering, turning left/right, and moving up/down, lead to significantly greater complexity compared to fixed-camera MOT.
IBD: Alleviating Hallucinations in Large Vision-Language Models via Image-Biased Decoding
Despite achieving rapid developments and with widespread applications, Large Vision-Language Models (LVLMs) confront a serious challenge of being prone to generating hallucinations.
RESMatch: Referring Expression Segmentation in a Semi-Supervised Manner
This pioneering work lays the groundwork for future research in semi-supervised learning for referring expression segmentation.
LLaFS: When Large Language Models Meet Few-Shot Segmentation
This paper proposes LLaFS, the first attempt to leverage large language models (LLMs) in few-shot segmentation.
Deep3DSketch+: Rapid 3D Modeling from Single Free-hand Sketches
In this work, we introduce a novel end-to-end approach, Deep3DSketch+, which performs 3D modeling using only a single free-hand sketch without inputting multiple sketches or view information.
Learning Gabor Texture Features for Fine-Grained Recognition
We innovatively utilize Gabor filters as a powerful extractor to exploit texture features, motivated by the capability of Gabor filters in effectively capturing multi-frequency features and detailed local information.
FVP: Fourier Visual Prompting for Source-Free Unsupervised Domain Adaptation of Medical Image Segmentation
In FVP, the visual prompt is parameterized using only a small amount of low-frequency learnable parameters in the input frequency space, and is learned by minimizing the segmentation loss between the predicted segmentation of the prompted target image and reliable pseudo segmentation label of the target image under the frozen model.
SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More
We can even outperform task-specific network models and achieve state-of-the-art performance in the task we tested: camouflaged object detection, shadow detection.
Continual Semantic Segmentation with Automatic Memory Sample Selection
Continual Semantic Segmentation (CSS) extends static semantic segmentation by incrementally introducing new classes for training.
Label-guided Attention Distillation for Lane Segmentation
In this paper, we address this issue by designing a distillation method that exploits label structure when training segmentation network.
Panoptic NeRF: 3D-to-2D Label Transfer for Panoptic Urban Scene Segmentation
In this work, we present a novel 3D-to-2D label transfer method, Panoptic NeRF, which aims for obtaining per-pixel 2D semantic and instance labels from easy-to-obtain coarse 3D bounding primitives.
Learning Statistical Texture for Semantic Segmentation
In this paper, we fully take advantages of the low-level texture features and propose a novel Statistical Texture Learning Network (STLNet) for semantic segmentation.
Distance Guided Channel Weighting for Semantic Segmentation
Recent works have achieved great success in improving the performance of multiple computer vision tasks by capturing features with a high channel number utilizing deep neural networks.
OGNet: Salient Object Detection with Output-guided Attention Module
However, most of the existing attention modules used in salient object detection are input with the processed feature map itself, which easily leads to the problem of `blind overconfidence'.
