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Found 15 papers, 6 papers with code
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.
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.
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 #6 on
Motion Synthesis
on KIT Motion-Language
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.
