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Found 16 papers, 12 papers with code
Improved Genetic Algorithm Based on Greedy and Simulated Annealing Ideas for Vascular Robot Ordering Strategy
To address these challenges, this research introduces a novel strategy, combining mathematical modeling, a hybrid genetic algorithm, and ARIMA time series forecasting.
LISA++: An Improved Baseline for Reasoning Segmentation with Large Language Model
While LISA effectively bridges the gap between segmentation and large language models to enable reasoning segmentation, it poses certain limitations: unable to distinguish different instances of the target region, and constrained by the pre-defined textual response formats.
LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models
For example, training on the context length of 8192 needs 16x computational costs in self-attention layers as that of 2048.
Mask-Attention-Free Transformer for 3D Instance Segmentation
Therefore, we abandon the mask attention design and resort to an auxiliary center regression task instead.
LISA: Reasoning Segmentation via Large Language Model
In this work, we propose a new segmentation task -- reasoning segmentation.
Spherical Transformer for LiDAR-based 3D Recognition
In this work, we study the varying-sparsity distribution of LiDAR points and present SphereFormer to directly aggregate information from dense close points to the sparse distant ones.
Ranked #1 on
Semantic Segmentation
on KITTI Semantic Segmentation
Learning Context-aware Classifier for Semantic Segmentation
Semantic segmentation is still a challenging task for parsing diverse contexts in different scenes, thus the fixed classifier might not be able to well address varying feature distributions during testing.
DecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentation
Unsupervised domain adaptation in semantic segmentation has been raised to alleviate the reliance on expensive pixel-wise annotations.
Stratified Transformer for 3D Point Cloud Segmentation
In this paper, we propose Stratified Transformer that is able to capture long-range contexts and demonstrates strong generalization ability and high performance.
Ranked #14 on
Semantic Segmentation
on ScanNet
Guided Point Contrastive Learning for Semi-supervised Point Cloud Semantic Segmentation
To address the high cost and challenges of 3D point-level labeling, we present a method for semi-supervised point cloud semantic segmentation to adopt unlabeled point clouds in training to boost the model performance.
Semi-supervised Semantic Segmentation with Directional Context-aware Consistency
Semantic segmentation has made tremendous progress in recent years.
3D Object Detection for Autonomous Driving: A Survey
Autonomous driving is regarded as one of the most promising remedies to shield human beings from severe crashes.
BADet: Boundary-Aware 3D Object Detection from Point Clouds
Specifically, instead of refining each proposal independently as previous works do, we represent each proposal as a node for graph construction within a given cut-off threshold, associating proposals in the form of local neighborhood graph, with boundary correlations of an object being explicitly exploited.
Social Link Inference via Multi-View Matching Network from Spatio-Temporal Trajectories
In this paper, we investigate the problem of social link inference in a target Location-aware Social Network (LSN), which aims at predicting the unobserved links between users within the network.
A Tree-structure Convolutional Neural Network for Temporal Features Exaction on Sensor-based Multi-resident Activity Recognition
With the propagation of sensor devices applied in smart home, activity recognition has ignited huge interest and most existing works assume that there is only one habitant.
Generalized Few-shot Semantic Segmentation
Then, since context is essential for semantic segmentation, we propose the Context-Aware Prototype Learning (CAPL) that significantly improves performance by 1) leveraging the co-occurrence prior knowledge from support samples, and 2) dynamically enriching contextual information to the classifier, conditioned on the content of each query image.
