4 papers
No time to train! Training-Free Reference-Based Instance Segmentation
Miguel Espinosa, Chenhongyi Yang, Linus Ericsson +2
The performance of image segmentation models has historically been constrained by the high cost of collecting large-scale annotated data. The Segment Anything Model (SAM) alleviate…
Evolutionary Architecture Search through Grammar-Based Sequence Alignment
Adri Gómez MartÃn, Felix Möller, Steven McDonagh +5
Neural architecture search (NAS) in expressive search spaces is a computationally hard problem, but it also holds the potential to automatically discover completely novel and perfo…
Exploiting Mixture-of-Experts Redundancy Unlocks Multimodal Generative Abilities
Raman Dutt, Harleen Hanspal, Guoxuan Xia +5
In this work, we undertake the challenge of augmenting the existing generative capabilities of pre-trained text-only large language models (LLMs) with multi-modal generation capabi…
There is no SAMantics! Exploring SAM as a Backbone for Visual Understanding Tasks
Miguel Espinosa, Chenhongyi Yang, Linus Ericsson +2
The Segment Anything Model (SAM) was originally designed for label-agnostic mask generation. Does this model also possess inherent semantic understanding, of value to broader visua…