3 citations · 3 across the 6 of their papers we have counts for
6 papers
OmniMouse: Scaling properties of multi-modal, multi-task Brain Models on 150B Neural Tokens
Konstantin F. Willeke, Polina Turishcheva, Alex Gilbert +18
Scaling data and artificial neural networks has transformed AI, driving breakthroughs in language and vision. Whether similar principles apply to modeling brain activity remains un…
Beyond Pixels: A Differentiable Pipeline for Probing Neuronal Selectivity in 3D
Pavithra Elumalai, Mohammad Bashiri, Goirik Chakrabarty +2
Visual perception relies on inference of 3D scene properties such as shape, pose, and lighting. To understand how visual sensory neurons enable robust perception, it is crucial to…
ReMOVE: A Reference-free Metric for Object Erasure
Aditya Chandrasekar, Goirik Chakrabarty, Jai Bardhan +2
We introduce , a novel reference-free metric for assessing object erasure efficacy in diffusion-based image editing models post-generation. Unlike existing measure…
LoMOE: Localized Multi-Object Editing via Multi-Diffusion
Goirik Chakrabarty, Aditya Chandrasekar, Ramya Hebbalaguppe +1
Recent developments in the field of diffusion models have demonstrated an exceptional capacity to generate high-quality prompt-conditioned image edits. Nevertheless, previous appro…
pSTarC: Pseudo Source Guided Target Clustering for Fully Test-Time Adaptation
Manogna Sreenivas, Goirik Chakrabarty, Soma Biswas
Test Time Adaptation (TTA) is a pivotal concept in machine learning, enabling models to perform well in real-world scenarios, where test data distribution differs from training. In…
SATA: Source Anchoring and Target Alignment Network for Continual Test Time Adaptation
Goirik Chakrabarty, Manogna Sreenivas, Soma Biswas
Adapting a trained model to perform satisfactorily on continually changing testing domains/environments is an important and challenging task. In this work, we propose a novel frame…