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Found 7 papers, 3 papers with code
SAM 2: Segment Anything in Images and Videos
We present Segment Anything Model 2 (SAM 2), a foundation model towards solving promptable visual segmentation in images and videos.
Ranked #1 on
Semi-Supervised Video Object Segmentation
on MOSE
(using extra training data)
Window Attention is Bugged: How not to Interpolate Position Embeddings
To fix it, we introduce a simple absolute window position embedding strategy, which solves the bug outright in Hiera and allows us to increase both speed and performance of the model in ViTDet.
Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
Modern hierarchical vision transformers have added several vision-specific components in the pursuit of supervised classification performance.
Ranked #1 on
Image Classification
on iNaturalist 2019
(using extra training data)
MAViL: Masked Audio-Video Learners
We present Masked Audio-Video Learners (MAViL) to train audio-visual representations.
Learning Background Invariance Improves Generalization and Robustness in Self-Supervised Learning on ImageNet and Beyond
Through a systematic, comprehensive investigation, we show that background augmentations lead to improved generalization with substantial improvements ($\sim$1-2% on ImageNet) in performance across a spectrum of state-of-the-art self-supervised methods (MoCo-v2, BYOL, SwAV) on a variety of tasks, even enabling performance on par with the supervised baseline.
Demystifying excessively volatile human learning: A Bayesian persistent prior and a neural approximation
We derive the theoretical relationship between DBM and EXP, and show that EXP gains computational efficiency by foregoing the representation of inferential uncertainty (as does the delta rule), but that it nevertheless achieves near-Bayesian performance due to its ability to incorporate a "persistent prior" influence unique to DBM and absent from the other algorithms.
Beauty-in-averageness and its contextual modulations: A Bayesian statistical account
This statistical coding cost account explains both BiA, where facial blends generally have higher likelihood than ``parent faces'', and UiA, when the preceding context or task restricts face representation to a task-relevant subset of features, thus redefining statistical typicality and encoding cost within that subspace.
