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Found 17 papers, 12 papers with code
Agents: An Open-source Framework for Autonomous Language Agents
Recent advances on large language models (LLMs) enable researchers and developers to build autonomous language agents that can automatically solve various tasks and interact with environments, humans, and other agents using natural language interfaces.
Adaptive Low Rank Adaptation of Segment Anything to Salient Object Detection
Foundation models, such as OpenAI's GPT-3 and GPT-4, Meta's LLaMA, and Google's PaLM2, have revolutionized the field of artificial intelligence.
P2C: Self-Supervised Point Cloud Completion from Single Partial Clouds
Point cloud completion aims to recover the complete shape based on a partial observation.
A Comprehensive Overview of Large Language Models
Considering the rapidly emerging plethora of literature on LLMs, it is imperative that the research community is able to benefit from a concise yet comprehensive overview of the recent developments in this field.
PointCaM: Cut-and-Mix for Open-Set Point Cloud Learning
Specifically, we use the Unknown-Point Simulator to simulate out-of-distribution data in the training stage by manipulating the geometric context of partial known data.
Energy-Based Residual Latent Transport for Unsupervised Point Cloud Completion
Unsupervised point cloud completion aims to infer the whole geometry of a partial object observation without requiring partial-complete correspondence.
Efficient Score Computation and Expectation-Maximization Algorithm in Regime-Switching Models
This study proposes an efficient algorithm for score computation for regime-switching models, and derived from which, an efficient expectation-maximization (EM) algorithm.
PU-Transformer: Point Cloud Upsampling Transformer
Given the rapid development of 3D scanners, point clouds are becoming popular in AI-driven machines.
PnP-3D: A Plug-and-Play for 3D Point Clouds
With the help of the deep learning paradigm, many point cloud networks have been invented for visual analysis.
Investigating Attention Mechanism in 3D Point Cloud Object Detection
Object detection in three-dimensional (3D) space attracts much interest from academia and industry since it is an essential task in AI-driven applications such as robotics, autonomous driving, and augmented reality.
Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion
Given the prominence of current 3D sensors, a fine-grained analysis on the basic point cloud data is worthy of further investigation.
Ranked #6 on
Semantic Segmentation
on Semantic3D
Dense-Resolution Network for Point Cloud Classification and Segmentation
Our DRNet is designed to learn local point features from the point cloud in different resolutions.
Ranked #22 on
3D Part Segmentation
on ShapeNet-Part
Geometric Back-projection Network for Point Cloud Classification
As the basic task of point cloud analysis, classification is fundamental but always challenging.
Ranked #27 on
3D Point Cloud Classification
on ModelNet40
Unconstrained Fashion Landmark Detection via Hierarchical Recurrent Transformer Networks
This work addresses unconstrained fashion landmark detection, where clothing bounding boxes are not provided in both training and test.
DeepFashion: Powering Robust Clothes Recognition and Retrieval With Rich Annotations
To demonstrate the advantages of DeepFashion, we propose a new deep model, namely FashionNet, which learns clothing features by jointly predicting clothing attributes and landmarks.
DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection
In this paper, we propose deformable deep convolutional neural networks for generic object detection.
DeepID-Net: multi-stage and deformable deep convolutional neural networks for object detection
In the proposed new deep architecture, a new deformation constrained pooling (def-pooling) layer models the deformation of object parts with geometric constraint and penalty.
