Search Results for author:
Found 5 papers, 4 papers with code
detrex: Benchmarking Detection Transformers
To address this issue, we develop a unified, highly modular, and lightweight codebase called detrex, which supports a majority of the mainstream DETR-based instance recognition algorithms, covering various fundamental tasks, including object detection, segmentation, and pose estimation.
Expediting Large-Scale Vision Transformer for Dense Prediction without Fine-tuning
Vision transformers have recently achieved competitive results across various vision tasks but still suffer from heavy computation costs when processing a large number of tokens.
DETRs with Hybrid Matching
One-to-one set matching is a key design for DETR to establish its end-to-end capability, so that object detection does not require a hand-crafted NMS (non-maximum suppression) to remove duplicate detections.
Learning Efficient Vision Transformers via Fine-Grained Manifold Distillation
Specifically, we train a tiny student model to match a pre-trained teacher model in the patch-level manifold space.
Correlational quantum theory and correlation constraints
A correlational dialect is introduced within the quantum theory language to give a unified treatment of finite-dimensional informational/operational quantum theories, infinite-dimensional relativistic quantum theories, and quantum gravity.
Quantum Physics General Relativity and Quantum Cosmology High Energy Physics - Theory
