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Found 11 papers, 0 papers with code
Motion Flow Matching for Human Motion Synthesis and Editing
In this paper, we propose \emph{Motion Flow Matching}, a novel generative model designed for human motion generation featuring efficient sampling and effectiveness in motion editing applications.
Guided Diffusion from Self-Supervised Diffusion Features
However, recent studies have revealed that the feature representation derived from diffusion model itself is discriminative for numerous downstream tasks as well, which prompts us to propose a framework to extract guidance from, and specifically for, diffusion models.
Shape or Texture: Understanding Discriminative Features in CNNs
Contrasting the previous evidence that neurons in the later layers of a Convolutional Neural Network (CNN) respond to complex object shapes, recent studies have shown that CNNs actually exhibit a `texture bias': given an image with both texture and shape cues (e. g., a stylized image), a CNN is biased towards predicting the category corresponding to the texture.
SLIM: Self-Supervised LiDAR Scene Flow and Motion Segmentation
Recently, several frameworks for self-supervised learning of 3D scene flow on point clouds have emerged.
Unsupervised Video Understanding by Reconciliation of Posture Similarities
Without any manual annotation, the model learns a structured representation of postures and their temporal development.
LSTM Self-Supervision for Detailed Behavior Analysis
Behavior analysis provides a crucial non-invasive and easily accessible diagnostic tool for biomedical research.
Deep Semantic Feature Matching
We introduce a novel method for semantic matching with pre-trained CNN features which is based on convolutional feature pyramids and activation guided feature selection.
Per-Sample Kernel Adaptation for Visual Recognition and Grouping
Nevertheless, the common approach to this problem in feature selection and kernel methods is to down-weight or eliminate entire training samples or the same dimensions of all samples.
Regularizing Max-Margin Exemplars by Reconstruction and Generative Models
Part-based models are one of the leading paradigms in visual recognition.
Randomized Max-Margin Compositions for Visual Recognition
A main theme in object detection are currently discriminative part-based models.
Visual Recognition using Embedded Feature Selection for Curvature Self-Similarity
This demand has led to feature descriptors of ever increasing dimensionality like co-occurrence statistics and self-similarity.
