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Found 29 papers, 13 papers with code
YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception
The YOLO series models reign supreme in real-time object detection due to their superior accuracy and computational efficiency.
SoftHGNN: Soft Hypergraph Neural Networks for General Visual Recognition
However, existing hypergraph neural networks typically rely on static and hard hyperedge assignments, leading to excessive and redundant hyperedges with hard binary vertex memberships that overlook the continuity of visual semantics.
ERetinex: Event Camera Meets Retinex Theory for Low-Light Image Enhancement
Additionally, we propose an effective fusion strategy that combines the high dynamic range data from event cameras with the color information of traditional images to enhance image quality.
Hypergraph Foundation Model
Hypergraph neural networks (HGNNs) effectively model complex high-order relationships in domains like protein interactions and social networks by connecting multiple vertices through hyperedges, enhancing modeling capabilities, and reducing information loss.
Jointly Understand Your Command and Intention:Reciprocal Co-Evolution between Scene-Aware 3D Human Motion Synthesis and Analysis
As two intimate reciprocal tasks, scene-aware human motion synthesis and analysis require a joint understanding between multiple modalities, including 3D body motions, 3D scenes, and textual descriptions.
EigenActor: Variant Body-Object Interaction Generation Evolved from Invariant Action Basis Reasoning
Specifically, the first canonical body action inference stage focuses on learning intra-class shareable body motion priors and mapping given text-based semantics to action-specific canonical 3D body motions.
Beyond Graphs: Can Large Language Models Comprehend Hypergraphs?
Existing benchmarks like NLGraph and GraphQA evaluate LLMs on graphs by focusing mainly on pairwise relationships, overlooking the high-order correlations found in real-world data.
Hyper-YOLO: When Visual Object Detection Meets Hypergraph Computation
We introduce Hyper-YOLO, a new object detection method that integrates hypergraph computations to capture the complex high-order correlations among visual features.
PARE-Net: Position-Aware Rotation-Equivariant Networks for Robust Point Cloud Registration
To further improve the distinctiveness of descriptors, we propose a position-aware convolution, which can better learn spatial information of local structures.
Multi-Condition Latent Diffusion Network for Scene-Aware Neural Human Motion Prediction
Inferring 3D human motion is fundamental in many applications, including understanding human activity and analyzing one's intention.
Semantic Flow: Learning Semantic Field of Dynamic Scenes from Monocular Videos
As there is 2D-to-3D ambiguity problem in the viewing direction when extracting 3D flow features from 2D video frames, we consider the volume densities as opacity priors that describe the contributions of flow features to the semantics on the frames.
Learning a Category-level Object Pose Estimator without Pose Annotations
Instead of using manually annotated images, we leverage diffusion models (e. g., Zero-1-to-3) to generate a set of images under controlled pose differences and propose to learn our object pose estimator with those images.
Hypergraph-based Multi-View Action Recognition using Event Cameras
By treating segments as vertices and constructing hyperedges using rule-based and KNN-based strategies, a multi-view hypergraph neural network that captures relationships across viewpoint and temporal features is established.
ModaLink: Unifying Modalities for Efficient Image-to-PointCloud Place Recognition
Experimental results on the KITTI dataset show that our proposed methods achieve state-of-the-art performance while running in real time.
GUESS:GradUally Enriching SyntheSis for Text-Driven Human Motion Generation
The whole text-driven human motion synthesis problem is then divided into multiple abstraction levels and solved with a multi-stage generation framework with a cascaded latent diffusion model: an initial generator first generates the coarsest human motion guess from a given text description; then, a series of successive generators gradually enrich the motion details based on the textual description and the previous synthesized results.
Ranked #15 on
Motion Synthesis
on KIT Motion-Language
ColorPCR: Color Point Cloud Registration with Multi-Stage Geometric-Color Fusion
In this way both geometric and color data can be used thus lead to robust performance even under extremely challenging scenarios such as low overlap between two point clouds.
3D Feature Tracking via Event Camera
To achieve this our method leverages a joint framework to predict the 2D feature motion offsets and the 3D feature spatial position simultaneously.
Learning Discriminative Features for Crowd Counting
Crowd counting models in highly congested areas confront two main challenges: weak localization ability and difficulty in differentiating between foreground and background, leading to inaccurate estimations.
Tolerating Annotation Displacement in Dense Object Counting via Point Annotation Probability Map
To improve the robustness to annotation displacement, we design an effective transport cost function based on GGD.
HD2Reg: Hierarchical Descriptors and Detectors for Point Cloud Registration
Feature Descriptors and Detectors are two main components of feature-based point cloud registration.
HybridPoint: Point Cloud Registration Based on Hybrid Point Sampling and Matching
Firstly, we propose to use salient points with prominent local features as nodes to increase patch repeatability, and introduce some uniformly distributed points to complete the point cloud, thus constituting hybrid points.
Decompose More and Aggregate Better: Two Closer Looks at Frequency Representation Learning for Human Motion Prediction
Encouraged by the effectiveness of encoding temporal dynamics within the frequency domain, recent human motion prediction systems prefer to first convert the motion representation from the original pose space into the frequency space.
MonoNeRF: Learning a Generalizable Dynamic Radiance Field from Monocular Videos
More specifically, we learn an implicit velocity field to estimate point trajectory from temporal features with Neural ODE, which is followed by a flow-based feature aggregation module to obtain spatial features along the point trajectory.
Counting Varying Density Crowds Through Density Guided Adaptive Selection CNN and Transformer Estimation
Thus, CNN could locate and estimate crowds accurately in low-density regions, while it is hard to properly perceive the densities in high-density regions.
C-CAM: Causal CAM for Weakly Supervised Semantic Segmentation on Medical Image
Our method is motivated by two cause-effect chains including category-causality chain and anatomy-causality chain.
Region-Aware Network: Model Human's Top-Down Visual Perception Mechanism for Crowd Counting
More specifically, we scan the whole input images and its priority maps in the form of column vector to obtain a relevance matrix estimating their similarity.
Color Point Cloud Registration Based on Supervoxel Correspondence
Different from traditional feature-based methods, we design a hybrid feature representation with color moments of the point, which could be applied naturally for any color point cloud.
An Effective Approach for Point Clouds Registration Based on the Hard and Soft Assignments
For the registration of partially overlapping point clouds, this paper proposes an effective approach based on both the hard and soft assignments.
