Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Revisiting the Integration of Convolution and Attention for Vision Backbone
Authors: Lei Zhu, Xinjiang Wang, Wayne Zhang, Rynson Lau
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experiments on various vision tasks, we empirically verify the potential of the proposed integration scheme, named GLMix |
| Researcher Affiliation | Collaboration | Lei Zhu City University of Hong Kong EMAIL Xinjiang Wang Sensetime Research EMAIL Wayne Zhang Sensetime Research EMAIL Rynson Lau City University of Hong Kong EMAIL |
| Pseudocode | No | The paper does not contain a block explicitly labeled as "Pseudocode" or "Algorithm", nor does it present structured steps in a pseudocode format. |
| Open Source Code | No | Code will be available at https://github.com/rayleizhu/GLMix. |
| Open Datasets | Yes | We conduct image classification experiments on the Image Net-1k dataset [13]... We evaluate the backbones for object detection and instance segmentation on COCO 2017 [31]... Our semantic segmentation experiments are conducted on the ADE20K dataset... |
| Dataset Splits | No | The paper refers to using "standard training recipes" and existing datasets like ImageNet-1k, COCO, and ADE20K, but it does not explicitly provide the specific validation dataset splits (percentages, counts, or explicit statements about using a predefined validation split from these datasets) within its own text. |
| Hardware Specification | Yes | following the same hardware (a single Tesla V100 32G GPU) and batch size (128) configurations used in Swin-Transformer [35]. |
| Software Dependencies | No | The paper mentions using "the timm library [54]", "the MMDetection [4] toolbox", and "the MMSegmentation [10] toolbox" but does not specify the version numbers for these software components. |
| Experiment Setup | Yes | For the standard supervised training recipe, training details are in Table 8. When training with the advanced distillation recipe [26], we add an extra distillation head to the GLNet-4G/9G model and use the NFNet-F6 [2] to generate distillation targets; other training details are shown in Table 9. |