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Found 13 papers, 7 papers with code
MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection
We propose a novel approach to video anomaly detection: we treat feature vectors extracted from videos as realizations of a random variable with a fixed distribution and model this distribution with a neural network.
Meta-Prompting for Automating Zero-shot Visual Recognition with LLMs
Prompt ensembling of Large Language Model (LLM) generated category-specific prompts has emerged as an effective method to enhance zero-shot recognition ability of Vision-Language Models (VLMs).
Sit Back and Relax: Learning to Drive Incrementally in All Weather Conditions
We propose to store these affine parameters as a memory bank for each weather condition and plug-in their weather-specific parameters during driving (i. e. test time) when the respective weather conditions are encountered.
MAtch, eXpand and Improve: Unsupervised Finetuning for Zero-Shot Action Recognition with Language Knowledge
We adapt a VL model for zero-shot and few-shot action recognition using a collection of unlabeled videos and an unpaired action dictionary.
Ranked #3 on
Zero-Shot Action Recognition
on Kinetics
Video Test-Time Adaptation for Action Recognition
Our proposed method demonstrates a substantial performance gain over existing test-time adaptation approaches in both evaluations of a single distribution shift and the challenging case of random distribution shifts.
ActMAD: Activation Matching to Align Distributions for Test-Time-Training
Test-Time-Training (TTT) is an approach to cope with out-of-distribution (OOD) data by adapting a trained model to distribution shifts occurring at test-time.
MATE: Masked Autoencoders are Online 3D Test-Time Learners
Our MATE is the first Test-Time-Training (TTT) method designed for 3D data, which makes deep networks trained for point cloud classification robust to distribution shifts occurring in test data.
Enforcing connectivity of 3D linear structures using their 2D projections
Many biological and medical tasks require the delineation of 3D curvilinear structures such as blood vessels and neurites from image volumes.
Localized Persistent Homologies for more Effective Deep Learning
Persistent Homologies have been successfully used to increase the performance of deep networks trained to detect curvilinear structures and to improve the topological quality of the results.
TopoAL: An Adversarial Learning Approach for Topology-Aware Road Segmentation
Instead, we use a more sophisticated discriminator that returns a label pyramid describing what portions of the road network are correct at several different scales.
Joint Segmentation and Path Classification of Curvilinear Structures
Detection of curvilinear structures in images has long been of interest.
Beyond the Pixel-Wise Loss for Topology-Aware Delineation
We propose a new loss term that is aware of the higher-order topological features of linear structures.
A MRF Shape Prior for Facade Parsing With Occlusions
We present a new shape prior formalism for segmentation of rectified facade images.
