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Found 8 papers, 6 papers with code
Effective Self-supervised Pre-training on Low-compute Networks without Distillation
This hints at view sampling being one of the performance bottlenecks for SSL on low-capacity networks.
iBoot: Image-bootstrapped Self-Supervised Video Representation Learning
Video self-supervised learning (SSL) suffers from added challenges: video datasets are typically not as large as image datasets, compute is an order of magnitude larger, and the amount of spurious patterns the optimizer has to sieve through is multiplied several fold.
EdgeViTs: Competing Light-weight CNNs on Mobile Devices with Vision Transformers
In this work, pushing further along this under-studied direction we introduce EdgeViTs, a new family of light-weight ViTs that, for the first time, enable attention-based vision models to compete with the best light-weight CNNs in the tradeoff between accuracy and on-device efficiency.
Instance-level Image Retrieval using Reranking Transformers
Instance-level image retrieval is the task of searching in a large database for images that match an object in a query image.
Ranked #3 on
Image Retrieval
on RParis (Medium)
Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning
Pseudo-labeling works by applying pseudo-labels to samples in the unlabeled set by using a model trained on the combination of the labeled samples and any previously pseudo-labeled samples, and iteratively repeating this process in a self-training cycle.
Drill-down: Interactive Retrieval of Complex Scenes using Natural Language Queries
We show that using multiple rounds of natural language queries as input can be surprisingly effective to find arbitrarily specific images of complex scenes.
Text2Scene: Generating Compositional Scenes from Textual Descriptions
In this paper, we propose Text2Scene, a model that generates various forms of compositional scene representations from natural language descriptions.
Where and Who? Automatic Semantic-Aware Person Composition
Image compositing is a method used to generate realistic yet fake imagery by inserting contents from one image to another.
