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Found 10 papers, 2 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.
ChangeNet: Multi-Temporal Asymmetric Change Detection Dataset
The ChangeNet dataset is suitable for both binary change detection (BCD) and semantic change detection (SCD) tasks.
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.
Guided Patch-Grouping Wavelet Transformer with Spatial Congruence for Ultra-High Resolution Segmentation
For the sake of high inference speed and low computation complexity, $\mathcal{T}$ partitions the original UHR image into patches and groups them dynamically, then learns the low-level local details with the lightweight multi-head Wavelet Transformer (WFormer) network.
Ultra-High Resolution Segmentation with Ultra-Rich Context: A Novel Benchmark
With the increasing interest and rapid development of methods for Ultra-High Resolution (UHR) segmentation, a large-scale benchmark covering a wide range of scenes with full fine-grained dense annotations is urgently needed to facilitate the field.
Ranked #1 on
Semantic Segmentation
on INRIA Aerial Image Labeling
(mIOU metric)
Structural and Statistical Texture Knowledge Distillation for Semantic Segmentation
Existing knowledge distillation works for semantic segmentation mainly focus on transferring high-level contextual knowledge from teacher to student.
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.
Context-Aware Graph Convolution Network for Target Re-identification
Most existing re-identification methods focus on learning robust and discriminative features with deep convolution networks.
Class-wise Dynamic Graph Convolution for Semantic Segmentation
Specifically, the CDGC module takes the coarse segmentation result as class mask to extract node features for graph construction and performs dynamic graph convolutions on the constructed graph to learn the feature aggregation and weight allocation.
