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Found 13 papers, 8 papers with code
SC-Tune: Unleashing Self-Consistent Referential Comprehension in Large Vision Language Models
In this paper, we present and delve into the self-consistency capability of LVLMs, a crucial aspect that reflects the models' ability to both generate informative captions for specific objects and subsequently utilize these captions to accurately re-identify the objects in a closed-loop process.
BjTT: A Large-scale Multimodal Dataset for Traffic Prediction
The key challenge of the TTG task is how to associate text with the spatial structure of the road network and traffic data for generating traffic situations.
RIME: Robust Preference-based Reinforcement Learning with Noisy Preferences
Our experiments on robotic manipulation and locomotion tasks demonstrate that RIME significantly enhances the robustness of the current state-of-the-art PbRL method.
Non-Neighbors Also Matter to Kriging: A New Contrastive-Prototypical Learning
As a pre-trained paradigm, we conduct the Kriging task from a new perspective of representation: we aim to first learn robust and general representations and then recover attributes from representations.
ChatTraffic: Text-to-Traffic Generation via Diffusion Model
The key challenge of the TTG task is how to associate text with the spatial structure of the road network and traffic data for generating traffic situations.
Multi-Task Learning-Enabled Automatic Vessel Draft Reading for Intelligent Maritime Surveillance
The accurate and efficient vessel draft reading (VDR) is an important component of intelligent maritime surveillance, which could be exploited to assist in judging whether the vessel is normally loaded or overloaded.
Point Cloud Classification Using Content-based Transformer via Clustering in Feature Space
To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short.
Ranked #9 on
3D Point Cloud Classification
on ScanObjectNN
Asynchronous Trajectory Matching-Based Multimodal Maritime Data Fusion for Vessel Traffic Surveillance in Inland Waterways
To further improve vessel traffic surveillance, it becomes necessary to fuse the AIS and video data to simultaneously capture the visual features, identity and dynamic information for the vessels of interest.
Conservative-Progressive Collaborative Learning for Semi-supervised Semantic Segmentation
Pseudo supervision is regarded as the core idea in semi-supervised learning for semantic segmentation, and there is always a tradeoff between utilizing only the high-quality pseudo labels and leveraging all the pseudo labels.
Deep Learning based Computer Vision Methods for Complex Traffic Environments Perception: A Review
This paper conducted an extensive literature review on the applications of computer vision in ITS and AD, and discusses challenges related to data, models, and complex urban environments.
A GAN-Based Short-Term Link Traffic Prediction Approach for Urban Road Networks Under a Parallel Learning Framework
This paper proposes a short-term traffic speed prediction approach, called PL-WGAN, for urban road networks, which is considered an important part of a novel parallel learning framework for traffic control and operation.
SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation
For each 3D point, the local polar representation block is firstly explored to construct a spatial representation that is invariant to the z-axis rotation, then the dual-distance attentive pooling block is designed to utilize the representations of its neighbors for learning more discriminative local features according to both the geometric and feature distances among them, and finally, the global contextual feature block is designed to learn a global context for each 3D point by utilizing its spatial location and the volume ratio of the neighborhood to the global point cloud.
Ranked #1 on
Semantic Segmentation
on Toronto-3D L002
FII-CenterNet: An Anchor-Free Detector With Foreground Attention for Traffic Object Detection
Most successful object detectors are anchor-based, which is difficult to adapt to the diversity of traffic objects.
