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NesT

37 papers tagged archive 2025-07-28

Introduced by Zizhao Zhang et al. in Nested Hierarchical Transformer: Towards Accurate, Data-Efficient and Interpretable Visual Understanding

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

NesT stacks canonical transformer layers to conduct local self-attention on every image block independently, and then "nests" them hierarchically. Coupling of processed information between spatially adjacent blocks is achieved through a proposed block aggregation between every two hierarchies. The overall hierarchical structure can be determined by two key hyper-parameters: patch size S × S and number of block hierarchies T_d. All blocks inside each hierarchy share one set of parameters. Given input of image, each image is linearly projected to an embedding. All embeddings are partitioned to blocks and flattened to generate final input. Each transformer layers is composed of a multi-head self attention (MSA) layer followed by a feed-forward fully-connected network (FFN) with skip-connection and Layer normalization. Positional embeddings are added to encode spatial information before feeding into the block. Lastly, a nested hierarchy with block aggregation is built -- every four spatially connected blocks are merged into one.

PaperSource

Papers archive 2025-07-28

30 shown of 37, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 35 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
regression3
Image Classification2
Object2
Prediction2
Anatomy1
Anomaly Detection1
Audio Denoising1
Autonomous Driving1
BIG-bench Machine Learning1
Benchmarking1
Decision Making1
Decoder1
Deep Learning1
Denoising1
Domain Generalization1
Event Detection1
Evolutionary Algorithms1
GPU1
Image Generation1
In-Context Learning1

Usage over time archive 2025-07-28

Papers per year tagged with NesT: 2021 to 2025, peak 12 12 0 2021: 5 papers 2021 2022: 7 papers 2022 2023: 10 papers 2023 2024: 12 papers 2024 2025: 3 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (37 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Vision Transformers

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