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Found 16 papers, 5 papers with code
Adapting Pre-Trained Vision Models for Novel Instance Detection and Segmentation
Novel Instance Detection and Segmentation (NIDS) aims at detecting and segmenting novel object instances given a few examples of each instance.
CaptainCook4D: A dataset for understanding errors in procedural activities
Following step-by-step procedures is an essential component of various activities carried out by individuals in their daily lives.
Self-Supervised Unseen Object Instance Segmentation via Long-Term Robot Interaction
By applying multi-object tracking and video object segmentation on the images collected via robot pushing, our system can generate segmentation masks of all the objects in these images in a self-supervised way.
Mean Shift Mask Transformer for Unseen Object Instance Segmentation
To illustrate the effectiveness of our method, we apply MSMFormer to unseen object instance segmentation.
Ranked #1 on
Unseen Object Instance Segmentation
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Relational Neural Markov Random Fields
Statistical Relational Learning (SRL) models have attracted significant attention due to their ability to model complex data while handling uncertainty.
Don't Explain without Verifying Veracity: An Evaluation of Explainable AI with Video Activity Recognition
The explanations generated by these simplified models, however, might not accurately justify and be truthful to the model.
Lifted Hybrid Variational Inference
We demonstrate that the proposed variational methods are both scalable and can take advantage of approximate model symmetries, even in the presence of a large amount of continuous evidence.
Learning Correlated Latent Representations with Adaptive Priors
Variational Auto-Encoders (VAEs) have been widely applied for learning compact, low-dimensional latent representations of high-dimensional data.
Correlated Variational Auto-Encoders
Variational Auto-Encoders (VAEs) are capable of learning latent representations for high dimensional data.
Scalable Neural Network Compression and Pruning Using Hard Clustering and L1 Regularization
We propose a simple and easy to implement neural network compression algorithm that achieves results competitive with more complicated state-of-the-art methods.
Automatic Parameter Tying in Neural Networks
Recently, there has been growing interest in methods that perform neural network compression, namely techniques that attempt to substantially reduce the size of a neural network without significant reduction in performance.
Sparse Approximate Conic Hulls
Equivalently, given the matrix X, consider the problem of finding a small subset, S, of the columns of X such that the conic hull of S \eps-approximates the conic hull of the columns of X, i. e., the distance of every column of X to the conic hull of the columns of S should be at most an \eps-fraction of the angular diameter of X.
Exactness of Approximate MAP Inference in Continuous MRFs
Computing the MAP assignment in graphical models is generally intractable.
Bethe Learning of Conditional Random Fields via MAP Decoding
Many machine learning tasks can be formulated in terms of predicting structured outputs.
Making Pairwise Binary Graphical Models Attractive
The later has better convergence properties but typically provides poorer estimates.
The Bethe Partition Function of Log-supermodular Graphical Models
Sudderth, Wainwright, and Willsky conjectured that the Bethe approximation corresponding to any fixed point of the belief propagation algorithm over an attractive, pairwise binary graphical model provides a lower bound on the true partition function.
