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Found 6 papers, 2 papers with code
Diffusion Models for Zero-Shot Open-Vocabulary Segmentation
This provides a distribution of appearances for a given text circumventing the ambiguity problem.
Unsupervised Multi-object Segmentation by Predicting Probable Motion Patterns
We propose a new approach to learn to segment multiple image objects without manual supervision.
Guess What Moves: Unsupervised Video and Image Segmentation by Anticipating Motion
Motion, measured via optical flow, provides a powerful cue to discover and learn objects in images and videos.
Ranked #4 on
Unsupervised Object Segmentation
on SegTrack-v2
ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation
We benchmark a large set of recent unsupervised multi-object segmentation models on ClevrTex and find all state-of-the-art approaches fail to learn good representations in the textured setting, despite impressive performance on simpler data.
Ranked #3 on
Unsupervised Object Segmentation
on ClevrTex
Automatic Inference of Cross-modal Connection Topologies for X-CNNs
The base approach learns the topology in a data-driven manner, by using measurements performed on the base CNN and supplied data.
Cross-modal Recurrent Models for Weight Objective Prediction from Multimodal Time-series Data
We analyse multimodal time-series data corresponding to weight, sleep and steps measurements.
