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arxiv: 1405.0312 · v3 · submitted 2014-05-01 · 💻 cs.CV

Recognition: no theorem link

Microsoft COCO: Common Objects in Context

Tsung-Yi Lin , Michael Maire , Serge Belongie , Lubomir Bourdev , Ross Girshick , James Hays , Pietro Perona , Deva Ramanan
show 2 more authors
C. Lawrence Zitnick Piotr Doll\'ar
Authors on Pith no claims yet

Pith reviewed 2026-05-11 23:56 UTC · model grok-4.3

classification 💻 cs.CV
keywords object recognitionscene understandinginstance segmentationcrowd sourcingcommon objectseveryday scenesCOCO datasetDeformable Parts Model
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The pith

A dataset of everyday scenes with per-instance segmentations advances object recognition in context.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper presents the COCO dataset to advance object recognition by situating it within scene understanding. Images of complex everyday scenes are gathered and objects labeled with per-instance segmentations for precise localization. The collection includes 91 common object types in 328,000 images totaling 2.5 million instances, created via novel crowd-sourcing interfaces. Statistical comparisons to existing datasets and baseline detection results using Deformable Parts Models are included. A reader would care if this enables models to better handle real-world variability and context.

Core claim

The authors present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. The dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of 2.5 million labeled instances in 328k images, the creation drew upon extensive crowd worker involvement via novel user interfaces for category detection, instanceスポ

What carries the argument

The per-instance segmentation labels on images of common objects in natural scenes, enabled by crowd-sourcing interfaces for detection, spotting, and segmentation.

If this is right

  • Object recognition models can be evaluated and trained on contextual scenes rather than cropped objects.
  • Per-instance masks support tasks requiring precise boundaries like segmentation.
  • Large scale supports statistical robustness in algorithm comparisons.
  • New annotation methods scale labeling to millions of instances efficiently.
  • Shifts focus toward holistic scene understanding in computer vision research.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This could improve transfer to applications like video analysis or robotics where context matters.
  • The dataset design may inspire similar collections for other modalities or languages.
  • One might test if models trained on COCO generalize better to unseen scenes than on prior datasets.

Load-bearing premise

The novel crowd-sourcing interfaces produce sufficiently accurate and consistent per-instance labels at the reported scale without substantial systematic errors.

What would settle it

An independent audit revealing high disagreement rates between the provided labels and expert annotations on a sample of images would falsify the dataset's utility for advancing reliable recognition.

read the original abstract

We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of 2.5 million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation. We present a detailed statistical analysis of the dataset in comparison to PASCAL, ImageNet, and SUN. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript presents the Microsoft COCO dataset: 328k images of complex everyday scenes with 2.5 million per-instance segmentations across 91 object categories recognizable by a 4-year-old. The central goal is to advance object recognition by embedding it in scene understanding, achieved via novel crowd-sourcing interfaces for category detection, instance spotting, and instance segmentation. The paper supplies statistical comparisons against PASCAL, ImageNet, and SUN plus baseline Deformable Parts Model results for bounding-box and segmentation detection.

Significance. If label quality holds, the dataset would be a major resource for the field: its scale, instance-level annotations in natural contexts, and explicit comparisons to prior collections provide a reproducible benchmark that can drive progress on precise localization and contextual recognition. The manuscript's strength lies in the independent construction and public release of the data together with parameter-free statistical characterizations.

major comments (1)
  1. [Dataset construction / annotation interfaces] Section on dataset construction and crowd-sourcing interfaces (description of instance spotting and segmentation UIs): no inter-annotator agreement scores, no expert-vs-crowd IoU on a held-out subset, and no error analysis for boundary precision or category confusion are reported for the 2.5M instances. Because the central claim rests on these labels enabling precise localization in scene context, the absence of quantitative validation is load-bearing; systematic biases (e.g., under-segmentation of small or occluded objects) would directly undermine downstream utility.
minor comments (2)
  1. [Baseline performance analysis] The baseline DPM results are presented mainly as reference points; a short discussion of why more contemporary detectors were not included would clarify the intended role of these numbers.
  2. [Statistical analysis] Tables and figures comparing statistics to PASCAL/ImageNet/SUN would benefit from explicit cross-references in the text and consistent axis scaling to ease reader comparison.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the positive assessment of the dataset's potential impact and for the constructive feedback on annotation validation. We address the major comment below and will revise the manuscript to incorporate the requested quantitative analysis.

read point-by-point responses

  1. Referee: [Dataset construction / annotation interfaces] Section on dataset construction and crowd-sourcing interfaces (description of instance spotting and segmentation UIs): no inter-annotator agreement scores, no expert-vs-crowd IoU on a held-out subset, and no error analysis for boundary precision or category confusion are reported for the 2.5M instances. Because the central claim rests on these labels enabling precise localization in scene context, the absence of quantitative validation is load-bearing; systematic biases (e.g., under-segmentation of small or occluded objects) would directly undermine downstream utility.

    Authors: We agree that quantitative validation of the annotations is essential to support claims about precise localization in scene context. In the revised manuscript we will add a dedicated subsection to the dataset construction section that reports (1) inter-annotator agreement scores for both instance spotting and segmentation tasks, computed on a held-out set of images using multiple independent workers; (2) expert-versus-crowd IoU statistics on a separate held-out subset of 500 images annotated by computer-vision experts; and (3) a systematic error analysis that quantifies boundary-precision errors and category confusions, with explicit breakdowns for small and occluded objects. These additions will directly address concerns about potential systematic biases and strengthen the reproducibility of the benchmark. revision: yes

Circularity Check

0 steps flagged

No circularity: dataset construction and release paper with external benchmarks

full rationale

The paper presents the COCO dataset, its collection process via crowd-sourcing interfaces, statistical comparisons to PASCAL/ImageNet/SUN, and baseline results using an off-the-shelf Deformable Parts Model. No derivations, equations, predictions, or first-principles claims exist that could reduce to fitted inputs, self-definitions, or self-citation chains. The contribution is the independent dataset itself; any questions of label accuracy fall under correctness risk rather than circularity per the analysis rules. Steps array left empty as no load-bearing reductions were identified.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a dataset construction paper with no mathematical derivations, fitted parameters, background axioms, or postulated entities beyond the empirical collection of images and labels.

pith-pipeline@v0.9.0 · 5480 in / 1095 out tokens · 42287 ms · 2026-05-11T23:56:56.623374+00:00 · methodology

discussion (0)

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