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..
COCO-Periph: Bridging the Gap Between Human and Machine Perception in the Periphery
Authors: Anne Harrington, Vasha DuTell, Mark Hamilton, Ayush Tewari, Simon Stent, William T. Freeman, Ruth Rosenholtz
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate this by (1) modifying the texture tiling model (TTM), a well tested model of peripheral vision, to be more flexibly used with DNNs, (2) generating a large dataset which we call COCO-Periph that contains images transformed to capture the information available in human peripheral vision, and (3) comparing DNNs to humans at peripheral object detection using a psychophysics experiment. |
| Researcher Affiliation | Collaboration | 1MIT CSAIL 2MIT Brain and Cognitive Sciences 3 Toyota Research Institute |
| Pseudocode | Yes | We provide pseudo-code for the machine psychophysics procedure. Algorithm 1 For each object present/absent image in the human experiment, we create 100 pairings of uniform TTM transform images (P and A). We simulate trials by looking at the box predictions (boxes) of a detection DNN for each pairing (p, a). |
| Open Source Code | Yes | We publicly release our COCO-Periph dataset, along with code for uniform TTM and the psychophysics analyses at https://github.com/Rosenholtz Lab/COCOPeriph to enable further research into human and machine perception in the periphery paving the way for DNNs to mimic and benefit from properties of human visual processing. |
| Open Datasets | Yes | We apply Uniform TTM to the COCO dataset, creating COCO-Periph which contains images transformed like peripheral vision. [...] COCO-Periph contains the entire COCO 2017 validation and test set, and over 74K, 117K, 118K, and 97K of the 118K total training images transformed to model 5 , 10 , 15 , and 20 of eccentricity, respectively. |
| Dataset Splits | Yes | COCO-Periph contains the entire COCO 2017 validation and test set |
| Hardware Specification | No | The paper mentions using 'MIT Super Cloud' and 'Lincoln Laboratory Supercomputing Center' for 'HPC resources' but does not specify exact hardware components like GPU/CPU models or specific configurations. |
| Software Dependencies | No | The paper mentions using Detectron2, the La Ma image in-painting model, Eye Link 1000, and the detrex library, but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | All models are trained for 180,000 iterations starting from the weights of a pretrained R-CNN from (Wu et al., 2019). We set the solver to step at 120,000 and 160,000. We set the base learning rate to 3 10 4. All other training parameters are the same R-CNN training parameters in (Wu et al., 2019) as the baseline model. |