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Found 13 papers, 7 papers with code
CLR: Channel-wise Lightweight Reprogramming for Continual Learning
Reprogramming parameters are task-specific and exclusive to each task, which makes our method immune to catastrophic forgetting.
Building One-class Detector for Anything: Open-vocabulary Zero-shot OOD Detection Using Text-image Models
Despite considerable effort, the problem remains significantly challenging in deep learning models due to their propensity to output over-confident predictions for OOD inputs.
Improving Zero-shot Generalization and Robustness of Multi-modal Models
We also show that our method improves across ImageNet shifted datasets, four other datasets, and other model architectures such as LiT.
Learning View-Disentangled Human Pose Representation by Contrastive Cross-View Mutual Information Maximization
To evaluate the power of the learned representations, in addition to the conventional fully-supervised action recognition settings, we introduce a novel task called single-shot cross-view action recognition.
View-Invariant, Occlusion-Robust Probabilistic Embedding for Human Pose
Recognition of human poses and actions is crucial for autonomous systems to interact smoothly with people.
Pose Augmentation: Class-agnostic Object Pose Transformation for Object Recognition
After training on unbalanced discrete poses (5 classes with 6 poses per object instance, plus 5 classes with only 2 poses), we show that OPT-Net can synthesize balanced continuous new poses along yaw and pitch axes with high quality.
View-Invariant Probabilistic Embedding for Human Pose
Depictions of similar human body configurations can vary with changing viewpoints.
Ranked #1 on
Pose Retrieval
on MPI-INF-3DHP
Improved Deep Learning of Object Category using Pose Information
Further more, we fine-tune object recognition on ImageNet by using the pretrained 2W-CNN and AlexNet features on iLab-20M, results show that significant improvements have been achieved, compared with training AlexNet from scratch.
Learning to Recognize Objects by Retaining other Factors of Variation
We show that disCNN achieves significantly better object recognition accuracies than AlexNet trained solely to predict object categories on the iLab-20M dataset, which is a large scale turntable dataset with detailed object pose and lighting information.
metricDTW: local distance metric learning in Dynamic Time Warping
We propose to learn multiple local Mahalanobis distance metrics to perform k-nearest neighbor (kNN) classification of temporal sequences.
Active Long Term Memory Networks
Continual Learning in artificial neural networks suffers from interference and forgetting when different tasks are learned sequentially.
shapeDTW: shape Dynamic Time Warping
Dynamic Time Warping (DTW) is an algorithm to align temporal sequences with possible local non-linear distortions, and has been widely applied to audio, video and graphics data alignments.
Fixation Bank: Learning to Reweight Fixation Candidates
Predicting where humans will fixate in a scene has many practical applications.
