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Found 9 papers, 0 papers with code
On Negative Sampling for Audio-Visual Contrastive Learning from Movies
The abundance and ease of utilizing sound, along with the fact that auditory clues reveal a plethora of information about what happens in a scene, make the audio-visual space an intuitive choice for representation learning.
Watching Too Much Television is Good: Self-Supervised Audio-Visual Representation Learning from Movies and TV Shows
The abundance and ease of utilizing sound, along with the fact that auditory clues reveal so much about what happens in the scene, make the audio-visual space a perfectly intuitive choice for self-supervised representation learning.
On Symbiosis of Attribute Prediction and Semantic Segmentation
In SSG, the same idea is applied to the intermediate layers of the network.
Training Faster by Separating Modes of Variation in Batch-normalized Models
We show that assuming samples within a mini-batch are from the same probability density function, then BN is identical to the Fisher vector of a Gaussian distribution.
Human Semantic Parsing for Person Re-identification
In this paper, we propose to adopt human semantic parsing which, due to its pixel-level accuracy and capability of modeling arbitrary contours, is naturally a better alternative.
Ranked #80 on
Person Re-Identification
on Market-1501
Improving Facial Attribute Prediction using Semantic Segmentation
We build our facial attribute prediction model jointly with a deep semantic segmentation network.
Ranked #2 on
Facial Attribute Classification
on LFWA
Understanding Trajectory Behavior: A Motion Pattern Approach
In the second phase, via a Kmeans clustering approach, we create motion components by clustering the flow vectors with respect to their location and velocity.
Recognition of Complex Events: Exploiting Temporal Dynamics between Underlying Concepts
While approaches based on bags of features excel at low-level action classification, they are ill-suited for recognizing complex events in video, where concept-based temporal representations currently dominate.
NMF-KNN: Image Annotation using Weighted Multi-view Non-negative Matrix Factorization
Such models become obsolete and require relearning when new images and tags are added to database.
