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Found 12 papers, 3 papers with code
ADer: A Comprehensive Benchmark for Multi-class Visual Anomaly Detection
This paper addresses this issue by proposing a comprehensive visual anomaly detection benchmark, \textbf{\textit{ADer}}, which is a modular framework that is highly extensible for new methods.
NoiseBoost: Alleviating Hallucination with Noise Perturbation for Multimodal Large Language Models
Multimodal large language models (MLLMs) contribute a powerful mechanism to understanding visual information building on large language models.
AdapNet: Adaptive Noise-Based Network for Low-Quality Image Retrieval
To mitigate this issue, we introduce a novel setting for low-quality image retrieval, and propose an Adaptive Noise-Based Network (AdapNet) to learn robust abstract representations.
Open-Vocabulary SAM3D: Understand Any 3D Scene
In this paper, we introduce OV-SAM3D, a universal framework for open-vocabulary 3D scene understanding.
PointRWKV: Efficient RWKV-Like Model for Hierarchical Point Cloud Learning
Transformers have revolutionized the point cloud learning task, but the quadratic complexity hinders its extension to long sequence and makes a burden on limited computational resources.
Single-temporal Supervised Remote Change Detection for Domain Generalization
In this paper, we propose a multimodal contrastive learning (ChangeCLIP) based on visual-language pre-training for change detection domain generalization.
Leveraging Fine-Grained Information and Noise Decoupling for Remote Sensing Change Detection
Next, a shape-aware and a brightness-aware module are designed to improve the capacity for representation learning.
MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection
Recent advancements in anomaly detection have seen the efficacy of CNN- and transformer-based approaches.
PointSeg: A Training-Free Paradigm for 3D Scene Segmentation via Foundation Models
On top of that, PointSeg can incorporate with various segmentation models and even surpasses the supervised methods.
UniM-OV3D: Uni-Modality Open-Vocabulary 3D Scene Understanding with Fine-Grained Feature Representation
3D open-vocabulary scene understanding aims to recognize arbitrary novel categories beyond the base label space.
Stereo RGB and Deeper LIDAR Based Network for 3D Object Detection
After aligning the interior points with fused features, the proposed network refines the prediction in a more accurate manner and encodes the whole box in a novel compact method.
SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds
Accurate 3D object detection from point clouds has become a crucial component in autonomous driving.
Ranked #1 on
3D Object Detection
on KITTI Pedestrians Hard
