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Found 6 papers, 4 papers with code
Indiscriminate Poisoning Attacks on Unsupervised Contrastive Learning
We propose Contrastive Poisoning (CP), the first effective such attack on CL.
Unsupervised Image Transformation Learning via Generative Adversarial Networks
In this work, we study the image transformation problem, which targets at learning the underlying transformations (e. g., the transition of seasons) from a collection of unlabeled images.
Delving into Deep Imbalanced Regression
We define Deep Imbalanced Regression (DIR) as learning from such imbalanced data with continuous targets, dealing with potential missing data for certain target values, and generalizing to the entire target range.
Complex Sequential Understanding through the Awareness of Spatial and Temporal Concepts
Understanding sequential information is a fundamental task for artificial intelligence.
Deep RNN Framework for Visual Sequential Applications
There are mainly two novel designs in our deep RNN framework: one is a new RNN module called Context Bridge Module (CBM) which splits the information flowing along the sequence (temporal direction) and along depth (spatial representation direction), making it easier to train when building deep by balancing these two directions; the other is the Overlap Coherence Training Scheme that reduces the training complexity for long visual sequential tasks on account of the limitation of computing resources.
Human Action Adverb Recognition: ADHA Dataset and A Three-Stream Hybrid Model
We introduce the first benchmark for a new problem --- recognizing human action adverbs (HAA): "Adverbs Describing Human Actions" (ADHA).
