Search Results for author:
Found 12 papers, 9 papers with code
RoDLA: Benchmarking the Robustness of Document Layout Analysis Models
To address this, we are the first to introduce a robustness benchmark for DLA models, which includes 450K document images of three datasets.
Skeleton-Based Human Action Recognition with Noisy Labels
In this study, we bridge this gap by implementing a framework that augments well-established skeleton-based human action recognition methods with label-denoising strategies from various research areas to serve as the initial benchmark.
Fourier Prompt Tuning for Modality-Incomplete Scene Segmentation
Integrating information from multiple modalities enhances the robustness of scene perception systems in autonomous vehicles, providing a more comprehensive and reliable sensory framework.
Navigating Open Set Scenarios for Skeleton-based Action Recognition
In real-world scenarios, human actions often fall outside the distribution of training data, making it crucial for models to recognize known actions and reject unknown ones.
Unveiling the Hidden Realm: Self-supervised Skeleton-based Action Recognition in Occluded Environments
To integrate action recognition methods into autonomous robotic systems, it is crucial to consider adverse situations involving target occlusions.
Elevating Skeleton-Based Action Recognition with Efficient Multi-Modality Self-Supervision
These works overlooked the differences in performance among modalities, which led to the propagation of erroneous knowledge between modalities while only three fundamental modalities, i. e., joints, bones, and motions are used, hence no additional modalities are explored.
Tightly-Coupled LiDAR-Visual SLAM Based on Geometric Features for Mobile Agents
On the other hand, the entire line segment detected by the visual subsystem overcomes the limitation of the LiDAR subsystem, which can only perform the local calculation for geometric features.
Open Scene Understanding: Grounded Situation Recognition Meets Segment Anything for Helping People with Visual Impairments
Grounded Situation Recognition (GSR) is capable of recognizing and interpreting visual scenes in a contextually intuitive way, yielding salient activities (verbs) and the involved entities (roles) depicted in images.
Delivering Arbitrary-Modal Semantic Segmentation
To make this possible, we present the arbitrary cross-modal segmentation model CMNeXt.
Ranked #1 on
Semantic Segmentation
on DSEC
CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers
Pixel-wise semantic segmentation of RGB images can be advanced by exploiting complementary features from the supplementary modality (X-modality).
Ranked #1 on
Semantic Segmentation
on SpectralWaste
TransKD: Transformer Knowledge Distillation for Efficient Semantic Segmentation
Semantic segmentation benchmarks in the realm of autonomous driving are dominated by large pre-trained transformers, yet their widespread adoption is impeded by substantial computational costs and prolonged training durations.
HIDA: Towards Holistic Indoor Understanding for the Visually Impaired via Semantic Instance Segmentation with a Wearable Solid-State LiDAR Sensor
To tackle these issues, we propose HIDA, a lightweight assistive system based on 3D point cloud instance segmentation with a solid-state LiDAR sensor, for holistic indoor detection and avoidance.
Ranked #16 on
3D Instance Segmentation
on ScanNet(v2)
