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Found 8 papers, 5 papers with code
Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach
Video Anomaly Detection (VAD) is an open-set recognition task, which is usually formulated as a one-class classification (OCC) problem, where training data is comprised of videos with normal instances while test data contains both normal and anomalous instances.
Unifying Synergies between Self-supervised Learning and Dynamic Computation
Computationally expensive training strategies make self-supervised learning (SSL) impractical for resource constrained industrial settings.
Motion Aware Self-Supervision for Generic Event Boundary Detection
In this work, we address this issue by revisiting a simple and effective self-supervised method and augment it with a differentiable motion feature learning module to tackle the spatial and temporal diversities in the GEBD task.
Is your noise correction noisy? PLS: Robustness to label noise with two stage detection
Experiments demonstrate the state-of-the-art performance of our Pseudo-Loss Selection (PLS) algorithm on a variety of benchmark datasets including curated data synthetically corrupted with in-distribution and out-of-distribution noise, and two real world web noise datasets.
Dynamic Channel Selection in Self-Supervised Learning
Currently, convnets pre-trained with self-supervision have obtained comparable performance on downstream tasks in comparison to their supervised counterparts in computer vision.
Discerning Generic Event Boundaries in Long-Form Wild Videos
Detecting generic, taxonomy-free event boundaries invideos represents a major stride forward towards holisticvideo understanding.
Evaluating Contrastive Models for Instance-based Image Retrieval
In this work, we evaluate contrastive models for the task of image retrieval.
Rethinking 360deg Image Visual Attention Modelling With Unsupervised Learning.
This performance is achieved using an encoder that is trained in a completely unsupervised way and a relatively lightweight supervised decoder (3. 8 X fewer parameters in the case of the ResNet50 encoder).
