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Found 8 papers, 1 papers with code
Ordinal Label Distribution Learning
Therefore, we propose a new paradigm, termed ordinal label distribution learning (OLDL).
S4OD: Semi-Supervised learning for Single-Stage Object Detection
In this paper, we design a dynamic self-adaptive threshold (DSAT) strategy in classification branch, which can automatically select pseudo labels to achieve an optimal trade-off between quality and quantity.
2nd Place Solution for VisDA 2021 Challenge -- Universally Domain Adaptive Image Recognition
The Visual Domain Adaptation (VisDA) 2021 Challenge calls for unsupervised domain adaptation (UDA) methods that can deal with both input distribution shift and label set variance between the source and target domains.
Affective Image Content Analysis: Two Decades Review and New Perspectives
Images can convey rich semantics and induce various emotions in viewers.
Multi-Source Domain Adaptation for Object Detection
To reduce annotation labor associated with object detection, an increasing number of studies focus on transferring the learned knowledge from a labeled source domain to another unlabeled target domain.
Computational Emotion Analysis From Images: Recent Advances and Future Directions
Recently, extensive research efforts have been dedicated to understanding the emotions of images.
Emotion-Based End-to-End Matching Between Image and Music in Valence-Arousal Space
In this paper, we study end-to-end matching between image and music based on emotions in the continuous valence-arousal (VA) space.
Attention-Aware Polarity Sensitive Embedding for Affective Image Retrieval
Images play a crucial role for people to express their opinions online due to the increasing popularity of social networks.
