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Found 15 papers, 10 papers with code
Unsupervised Video Understanding by Reconciliation of Posture Similarities
Without any manual annotation, the model learns a structured representation of postures and their temporal development.
Unsupervised Part-Based Disentangling of Object Shape and Appearance
Large intra-class variation is the result of changes in multiple object characteristics.
Ranked #3 on
Unsupervised Human Pose Estimation
on Human3.6M
Unsupervised Representation Learning by Discovering Reliable Image Relations
To nevertheless find those relations which can be reliably utilized for learning, we follow a divide-and-conquer strategy: We find reliable similarities by extracting compact groups of images and reliable dissimilarities by partitioning these groups into subsets, converting the complicated overall problem into few reliable local subproblems.
Revisiting Training Strategies and Generalization Performance in Deep Metric Learning
Deep Metric Learning (DML) is arguably one of the most influential lines of research for learning visual similarities with many proposed approaches every year.
PADS: Policy-Adapted Sampling for Visual Similarity Learning
Learning visual similarity requires to learn relations, typically between triplets of images.
Ranked #17 on
Metric Learning
on CUB-200-2011
(using extra training data)
Sharing Matters for Generalization in Deep Metric Learning
The common paradigm is discriminative metric learning, which seeks an embedding that separates different training classes.
DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning
Visual Similarity plays an important role in many computer vision applications.
Ranked #13 on
Metric Learning
on CUB-200-2011
(using extra training data)
S2SD: Simultaneous Similarity-based Self-Distillation for Deep Metric Learning
Deep Metric Learning (DML) provides a crucial tool for visual similarity and zero-shot applications by learning generalizing embedding spaces, although recent work in DML has shown strong performance saturation across training objectives.
Ranked #10 on
Metric Learning
on CARS196
(using extra training data)
Behavior-Driven Synthesis of Human Dynamics
Using this representation, we are able to change the behavior of a person depicted in an arbitrary posture, or to even directly transfer behavior observed in a given video sequence.
Stochastic Image-to-Video Synthesis using cINNs
Video understanding calls for a model to learn the characteristic interplay between static scene content and its dynamics: Given an image, the model must be able to predict a future progression of the portrayed scene and, conversely, a video should be explained in terms of its static image content and all the remaining characteristics not present in the initial frame.
Understanding Object Dynamics for Interactive Image-to-Video Synthesis
Given a static image of an object and a local poking of a pixel, the approach then predicts how the object would deform over time.
iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis
There will be distinctive movement, despite evident variations caused by the stochastic nature of our world.
Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning
Finally, we propose few-shot DML as an efficient way to consistently improve generalization in response to unknown test shifts presented in ooDML.
Cross-Image-Attention for Conditional Embeddings in Deep Metric Learning
Experiments on established DML benchmarks show that our cross-attention conditional embedding during training improves the underlying standard DML pipeline significantly so that it outperforms the state-of-the-art.
RudolfV: A Foundation Model by Pathologists for Pathologists
Histopathology plays a central role in clinical medicine and biomedical research.
