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Found 7 papers, 2 papers with code
Learning Long-Term Spatial-Temporal Graphs for Active Speaker Detection
Active speaker detection (ASD) in videos with multiple speakers is a challenging task as it requires learning effective audiovisual features and spatial-temporal correlations over long temporal windows.
Ranked #1 on
Node Classification
on AVA
Learning Spatial-Temporal Graphs for Active Speaker Detection
We address the problem of active speaker detection through a new framework, called SPELL, that learns long-range multimodal graphs to encode the inter-modal relationship between audio and visual data.
Incorporating Scalability in Unsupervised Spatio-Temporal Feature Learning
This necessitates learning of visual features from videos in an unsupervised setting.
W-TALC: Weakly-supervised Temporal Activity Localization and Classification
Most activity localization methods in the literature suffer from the burden of frame-wise annotation requirement.
Ranked #1 on
Action Classification
on ActivityNet-1.2
Exploiting Transitivity for Learning Person Re-Identification Models on a Budget
Minimization of labeling effort for person re-identification in camera networks is an important problem as most of the existing popular methods are supervised and they require large amount of manual annotations, acquiring which is a tedious job.
WEPSAM: Weakly Pre-Learnt Saliency Model
Recent thrust in saliency prediction research is to learn high level semantics using ground truth eye fixation datasets.
Visual saliency detection: a Kalman filter based approach
In this paper we propose a Kalman filter aided saliency detection model which is based on the conjecture that salient regions are considerably different from our "visual expectation" or they are "visually surprising" in nature.
