266 citations · 365 across the 9 of their papers we have counts for
9 papers
The SA-FARI Dataset: Segment Anything in Footage of Animals for Recognition and Identification
Dante Francisco Wasmuht, Otto Brookes, Maximillian Schall +21
Automated video analysis is critical for wildlife conservation. A foundational task in this domain is multi-animal tracking (MAT), which underpins applications such as individual r…
SAM 3: Segment Anything with Concepts
Nicolas Carion, Laura Gustafson, Yuan-Ting Hu +35
We present Segment Anything Model (SAM) 3, a unified model that detects, segments, and tracks objects in images and videos based on concept prompts, which we define as either short…
SAM 2: Segment Anything in Images and Videos
Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu +15
We present Segment Anything Model 2 (SAM 2), a foundation model towards solving promptable visual segmentation in images and videos. We build a data engine, which improves model an…
Window Attention is Bugged: How not to Interpolate Position Embeddings
Daniel Bolya, Chaitanya Ryali, Judy Hoffman +1
Window attention, position embeddings, and high resolution finetuning are core concepts in the modern transformer era of computer vision. However, we find that naively combining th…
Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
Chaitanya Ryali, Yuan-Ting Hu, Daniel Bolya +10
Modern hierarchical vision transformers have added several vision-specific components in the pursuit of supervised classification performance. While these components lead to effect…
MAViL: Masked Audio-Video Learners
Po-Yao Huang, Vasu Sharma, Hu Xu +7
We present Masked Audio-Video Learners (MAViL) to train audio-visual representations. Our approach learns with three complementary forms of self-supervision: (1) reconstruction of…
Characterizing and Improving the Robustness of Self-Supervised Learning through Background Augmentations
Chaitanya K. Ryali, David J. Schwab, Ari S. Morcos
Recent progress in self-supervised learning has demonstrated promising results in multiple visual tasks. An important ingredient in high-performing self-supervised methods is the u…
Can a Fruit Fly Learn Word Embeddings?
Yuchen Liang, Chaitanya K. Ryali, Benjamin Hoover +4
The mushroom body of the fruit fly brain is one of the best studied systems in neuroscience. At its core it consists of a population of Kenyon cells, which receive inputs from mult…
Bio-Inspired Hashing for Unsupervised Similarity Search
Chaitanya K. Ryali, John J. Hopfield, Leopold Grinberg +1
The fruit fly Drosophila's olfactory circuit has inspired a new locality sensitive hashing (LSH) algorithm, FlyHash. In contrast with classical LSH algorithms that produce low dime…