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Found 30 papers, 20 papers with code
From EduVisBench to EduVisAgent: A Benchmark and Multi-Agent Framework for Pedagogical Visualization
While foundation models (FMs), such as diffusion models and large vision-language models (LVLMs), have been widely applied in educational contexts, their ability to generate pedagogically effective visual explanations remains limited.
PHYBench: Holistic Evaluation of Physical Perception and Reasoning in Large Language Models
Current benchmarks for evaluating the reasoning capabilities of Large Language Models (LLMs) face significant limitations: task oversimplification, data contamination, and flawed evaluation items.
Rethinking End-to-End 2D to 3D Scene Segmentation in Gaussian Splatting
Therefore, we formulate the association learning module and the noisy label filtering module for effective and robust codebook learning.
KDSelector: A Knowledge-Enhanced and Data-Efficient Model Selector Learning Framework for Time Series Anomaly Detection
Model selection has been raised as an essential problem in the area of time series anomaly detection (TSAD), because there is no single best TSAD model for the highly heterogeneous time series in real-world applications.
SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines
To address this gap, we present SuperGPQA, a comprehensive benchmark that evaluates graduate-level knowledge and reasoning capabilities across 285 disciplines.
MJ-VIDEO: Fine-Grained Benchmarking and Rewarding Video Preferences in Video Generation
Recent advancements in video generation have significantly improved the ability to synthesize videos from text instructions.
Creating Virtual Environments with 3D Gaussian Splatting: A Comparative Study
3D Gaussian Splatting (3DGS) has recently emerged as an innovative and efficient 3D representation technique.
MS2Mesh-XR: Multi-modal Sketch-to-Mesh Generation in XR Environments
We present MS2Mesh-XR, a novel multi-modal sketch-to-mesh generation pipeline that enables users to create realistic 3D objects in extended reality (XR) environments using hand-drawn sketches assisted by voice inputs.
Advancing Extended Reality with 3D Gaussian Splatting: Innovations and Prospects
3D Gaussian Splatting (3DGS) has attracted significant attention for its potential to revolutionize 3D representation, rendering, and interaction.
NumGrad-Pull: Numerical Gradient Guided Tri-plane Representation for Surface Reconstruction from Point Clouds
Recent advancements address this problem by training neural signed distance functions to pull 3D location queries to their closest points on a surface, following the predicted signed distances and the analytical gradients computed by the network.
MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large Vision-Language Models
Extensive experiments demonstrate the effectiveness of our benchmark and metrics in providing a comprehensive evaluation of interleaved LVLMs.
PCF-Lift: Panoptic Lifting by Probabilistic Contrastive Fusion
Panoptic lifting is an effective technique to address the 3D panoptic segmentation task by unprojecting 2D panoptic segmentations from multi-views to 3D scene.
Improving Bird's Eye View Semantic Segmentation by Task Decomposition
In the first stage, we train a BEV autoencoder to reconstruct the BEV segmentation maps given corrupted noisy latent representation, which urges the decoder to learn fundamental knowledge of typical BEV patterns.
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
Large Language Models (LLMs) have recently demonstrated remarkable capabilities in natural language processing tasks and beyond.
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 #25 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 #39 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.
Ranked #1 on
2D Cyclist Detection
on ^(#$!@#$)(()))******
(using extra training data)
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.
