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Found 11 papers, 6 papers with code
SynCDR : Training Cross Domain Retrieval Models with Synthetic Data
In cross-domain retrieval, a model is required to identify images from the same semantic category across two visual domains.
Learning Human Action Recognition Representations Without Real Humans
To this end, we present, for the first time, a benchmark that leverages real-world videos with humans removed and synthetic data containing virtual humans to pre-train a model.
VisDA 2022 Challenge: Domain Adaptation for Industrial Waste Sorting
To test the abilities of computer vision models on this task, we present the VisDA 2022 Challenge on Domain Adaptation for Industrial Waste Sorting.
Fine-grained Few-shot Recognition by Deep Object Parsing
An object is parsed by estimating the locations of these K parts and a set of active templates that can reconstruct the part features.
Learning Compositional Representations for Effective Low-Shot Generalization
RPC encodes images by first decomposing them into salient parts, and then encoding each part as a mixture of a small number of prototypes, each representing a certain concept.
Task2Sim: Towards Effective Pre-Training and Transfer From Synthetic Data
It is thus better to tailor synthetic pre-training data to a specific downstream task, for best performance.
Task2Sim : Towards Effective Pre-training and Transfer from Synthetic Data
It is thus better to tailor synthetic pre-training data to a specific downstream task, for best performance.
VisDA-2021 Competition Universal Domain Adaptation to Improve Performance on Out-of-Distribution Data
Progress in machine learning is typically measured by training and testing a model on the same distribution of data, i. e., the same domain.
Effectively Leveraging Attributes for Visual Similarity
This enables our model to identify that two images contain the same attribute, but can have it deemed irrelevant (e. g., due to fine-grained differences between them) and ignored for measuring similarity between the two images.
Surprisingly Simple Semi-Supervised Domain Adaptation with Pretraining and Consistency
With our Pretraining and Consistency (PAC) approach, we achieve state of the art target accuracy on this semi-supervised domain adaptation task, surpassing multiple adversarial domain alignment methods, across multiple datasets.
Semi-supervised Domain Adaptation
Unsupervised Domain Adaptation
Self-supervised Visual Attribute Learning for Fashion Compatibility
Our approach consists of three self-supervised tasks designed to capture different concepts that are neglected in prior work that we can select from depending on the needs of our downstream tasks.
