standOff, and the article is at its publisher via the link below. When we have changed our mind. Some entries in the table begin with a word in capitals and the reason for it. EVIDENCE CORRECTED — the finding still stands, but we had quoted the wrong sentence from the paper, and the quotation has been replaced with the right one. RETRACTED — the entry itself was wrong; it stays on the page, marked, with the reason, rather than being deleted. WITHDRAWN AND REPLACED — the entry was wrong, and a corrected version of it appears in the entries below. Each mark carries its own reason, in brackets, because the reasons differ from one entry to the next. An entry we have WITHDRAWN is removed from this page and kept in the record, with its reason. A line reading Audit and a date is a later re-reading of the entry against its whole sentence: DOUBTFUL means it was kept, and the reason says what is loose about it. Quotation marks inside the tagger's sentence. Where a tag is the tagger's own sentence, a phrase in quotation marks inside it is borrowed from the paper — the tagger's wording around it is not. The paper's own sentence is the evidence quote for that row; on this page it is withheld with the article, and the article is at its publisher. Nothing inside quotation marks anywhere on this page is ours: if it is quoted, it is the paper's. Whose words a concept carries. A concept entry records a term the paper itself uses, so the term, the sense given for it, and its evidence are the paper's own words, quoted where the paper fixes them. This is the opposite of a sense entry in the tag table, which is the tagger's sentence about how the paper uses a term. Where a concept entry could not be grounded in the paper's words, the entry says so on its own row.| relation to other work | claim_relations | how this paper stands to another: agrees, contradicts, extends, qualifies |
| claim type | claim_type | the shape of the claim: existence, mechanism, method, critique, prediction, null |
| concept edge | concept_edges | an asserted relation between two concepts, with the sentence asserting it |
| concept | concepts | an idea the paper works with, quoted where the paper uses it |
| content tag | content_tags | a finding, definition, quantity, statistic or variable named in the paper |
| contribution | contribution | what the paper claims to add |
| control | controls | an experiment run to eliminate a rival explanation |
| limitation | limitations | what the paper says its evidence cannot support |
| mechanism | mechanisms | how the reported effect is said to come about |
| method | method | how the paper produced its evidence |
| object of study | object_of_study | what the paper is about — the thing it investigates |
| phenomenon | phenomenon | the effect or regularity under study |
| research question | research_question | the question the paper sets itself |
| sense | senses | a distinct use of a term, quoted where the paper fixes it |
| transfer | transfers | a method or concept carried in from another field |
Relations on this page carry a quotation for the claim. Where a relation was separately evidenced — and so tested against the vocabulary for its type — the row says so; 4 of 4 relations here carry that mark. The rest are the tagger’s reading of the sentence quoted beside them.
| facet | tag | field | conf. | whose words |
|---|---|---|---|---|
| object_of_study | generic object detection with deep learning - what the problem is, how it is scored, and what more than 300 papers have done to it Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | object_of_study | the paper's words | |
| research_question | what the field has achieved since deep learning arrived, organised by detection framework, feature representation, proposal generation, context modelling, training strategy and evaluation metric Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | research_question | the paper's words | |
| contribution | a survey of more than 300 contributions with an explicit account of how detection is evaluated - including that a detection counts as correct only above a chosen overlap threshold, and that the threshold changed Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | contribution | the paper's words | |
| claim_type | critique Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | claim_type | the paper's words | |
| phenomenon | a field whose measure of success is a threshold and whose object vocabulary is a benchmark's class list - 20, 200 or 91 depending which benchmark Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | phenomenon | the paper's words | |
| method | review Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: review | method | the paper's words | |
| mechanisms | a detection counts as a true positive when its category matches AND its overlap with the ground-truth box is not smaller than a predefined threshold. Every precision, recall and AP number in the field is downstream of that number - and the number is a convention, not a derivation Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: correct is a threshold | mechanisms[0] | 0.95 | the tagger's words |
| mechanisms | PASCAL VOC's mAP is computed at a single IoU of 0.5; MS COCO's is averaged across 0.5 to 0.95 in steps of 0.05. Both are called mAP. A survey of the field has to print a table of which benchmark means which, and any comparison across that boundary is comparing two definitions Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: the same metric name, two quantities | mechanisms[1] | 0.95 | the tagger's words |
| mechanisms | 41% of MS COCO objects are small and 24% are large, so size-stratified metrics were introduced. THE AGGREGATE WAS MISLEADING BECAUSE OF WHAT THE DATASET CONTAINED, and the response was to change the measure rather than the data Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | mechanisms[2] | 0.95 | the tagger's words |
| mechanisms | 20 classes in PASCAL VOC, 200 in ILSVRC, 91 in MS COCO. `Object detection` means detection of whichever of those lists the paper trained against, and the survey says plainly that all of them are much smaller than what a human recognises Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | mechanisms[3] | 0.95 | the tagger's words |
| mechanisms | one figure panel shows a single class spanning wildly different images; another shows four near-identical images from four different classes. So a detector cannot be evaluated on visual similarity, and the annotation is the only ground truth there is Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | mechanisms[4] | 0.95 | the tagger's words |
| mechanisms | THE LONGEST TERM LIST IN THE CORPUS, AND IT IS NOT A LIST OF SYNONYMS - IT IS A LIST OF TERMS WHOSE BOUNDARIES DISAGREE BETWEEN PAPERS, AND IT SITS IN A FOOTNOTE: `Quotation withheld`. Ten terms, attributed, declared unagreed, in a footnote - and the survey then has to pick. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | mechanisms[5] | 0.95 | the tagger's words |
| limitations | whether a detection is correct depends on a predefined overlap threshold rather than on any property of the image Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | limitations[0] | 0.95 | the tagger's words |
| limitations | mAP means different things under PASCAL VOC and MS COCO, so numbers are not comparable across that boundary Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | limitations[1] | 0.95 | the tagger's words |
| limitations | the number of object categories in existing benchmark datasets is much smaller than can be recognized by humans - 20, 200 and 91 against a real-world order of 10^4-10^5 Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | limitations[2] | 0.95 | the tagger's words |
| limitations | the challenge winner is decided by the number of object categories won, with mAP used as a team measure justified by the two rankings having always agreed Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | limitations[3] | 0.95 | the tagger's words |
| limitations | high accuracy and high efficiency are competing goals, so any single reported figure is a position on a trade-off rather than a level of performance Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | limitations[4] | 0.95 | the tagger's words |
| transfers | precision and recall from information retrieval, carried into vision as the basis of average precision and then of the field's single headline number Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | transfers[0] | 0.95 | the tagger's words |
| controls | MS COCO's average over ten IoU thresholds instead of a single 0.5 Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. eliminates: a detector tuned to clear one overlap value - averaging across 0.5 to 0.95 prices localisation accuracy into the score · outcome: held | controls[0] | 0.95 | the tagger's words |
| controls | size-stratified metrics introduced alongside the aggregate, after the object size distribution of the dataset was measured Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. eliminates: a headline number dominated by the size class that happens to be most common - 41% of MS COCO objects are small · outcome: held | controls[1] | 0.95 | the tagger's words |
| controls | the challenge's two rankings compared - by mAP and by number of object categories won - before adopting mAP as the team measure Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. eliminates: an aggregate standing in for per-category performance without evidence; the justification is that the two rankings were always the same · outcome: held | controls[2] | 0.95 | the tagger's words |
| controls | AP evaluated in a category-specific manner and only then averaged, rather than pooled across categories Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. eliminates: a common category's performance swamping a rare one in the aggregate · outcome: held | controls[3] | 0.95 | the tagger's words |
| controls | figure panels showing one class across wildly different appearances and four classes across near-identical appearances Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. outcome (stated by the tagger, no quotation): absent-declared-unreported | controls[4] | 0.95 | the tagger's words |
| concepts | IOU Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: IOU | concepts[0] | 0.95 | the paper's words |
| concepts | True Positive Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: True Positive | concepts[1] | 0.95 | the paper's words |
| concepts | mAP Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: mAP | concepts[2] | 0.95 | the paper's words |
| concepts | MS COCO Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: MS COCO | concepts[3] | 0.95 | the paper's words |
| concepts | object categories Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: object categories | concepts[4] | 0.95 | the paper's words |
| concepts | appearance Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: appearance | concepts[5] | 0.95 | the paper's words |
| concepts | object class Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: object class | concepts[6] | 0.95 | the paper's words |
| concepts | intraclass variations Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: intraclass variations | concepts[7] | 0.95 | the paper's words |
| concepts | accuracy Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: accuracy | concepts[8] | 0.95 | the paper's words |
| concepts | efficiency Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: efficiency | concepts[9] | 0.95 | the paper's words |
| concepts | specific instances Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: specific instances | concepts[10] | 0.95 | the paper's words |
| concepts | broad categories Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: broad categories | concepts[11] | 0.95 | the paper's words |
| senses | a correct detection - AS DEFINED: the predicted category equals the ground truth label AND the intersection-over-union with the ground-truth box is not smaller than a predefined threshold Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | senses[0] | 0.95 | the tagger's words |
| senses | mAP - UNDER PASCAL VOC: computed at a single IoU of 0.5 Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | senses[1] | 0.95 | the tagger's words |
| senses | mAP - UNDER MS COCO: averaged across IoU values from 0.5 to 0.95 in steps of 0.05, plus separate metrics for small, medium and large objects Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | senses[2] | 0.95 | the tagger's words |
| senses | object detection - OF SPECIFIC INSTANCES: matching a particular object, such as one face or one tower Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | senses[3] | 0.95 | the tagger's words |
| senses | object detection - OF BROAD CATEGORIES: the generic problem this survey is about, whose category list is whatever the benchmark defines Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | senses[4] | 0.95 | the tagger's words |
| senses | an object class - AS A VISUAL GROUPING: what the four similar-looking images in Fig. 6j would suggest, and which the caption says is wrong Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | senses[5] | 0.95 | the tagger's words |
| senses | an object class - AS A LABEL: a benchmark's annotation, which can hold an astonishing variation in appearance and can separate images that look alike Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | senses[6] | 0.95 | the tagger's words |
| senses | TEN NAMES FOR OVERLAPPING TASKS, AND THE FIELD IS DECLARED TO HAVE NO AGREEMENT ABOUT ANY OF THEM: `there is no universal agreement in the literature on the definitions of various vision subtasks. Terms such as detection, localization, recognition, classification, categorization, verification, identification, annotation, labeling, and understanding are often differently defined`. A survey cannot proceed without saying which it means, and the field supplies ten words and no map. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | senses[7] | 0.95 | the tagger's words |
| claim_relations | critiques → the benchmark class lists themselves - 20, 200 and 91 categories against a real world of 10^4 to 10^5 Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | claim_relations[0] | 0.95 | the tagger's words |
| claim_relations | qualifies → mAP as a comparable number, by showing that PASCAL VOC and MS COCO compute it at different IoU regimes Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | claim_relations[1] | 0.95 | the tagger's words |
| claim_relations | builds-on → more than 300 research contributions, organised by component of the detection pipeline Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | claim_relations[2] | 0.95 | the tagger's words |
| claim_relations | distinguishes-from → detection of specific instances, which the survey separates out as essentially a matching problem Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | claim_relations[3] | 0.95 | the tagger's words |
| concept_edges | appearance —distinct-from→ object class Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. differs in: whether two images look alike or carry the same label · the relation itself is separately evidenced · strength of the relation: 0.95 · differs in (stated by the tagger, no quotation): whether two images look alike or carry the same label · the relation stated: one class holds an astonishing variation in appearance, and four near-identical images belong to four different classes - the label is not recoverable from how things look, in either direction
Audit 2026-09-28: DOUBTFUL - read against its span and kept, but the span contrasts intra-class variation with inter-class similarity; distinct-from is loose. also in: appearance · object class | concept_edges[1] | 0.95 | the tagger's words |
| concept_edges | intraclass variations —distinct-from→ object categories Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. differs in: whether the difficulty is within one label or between labels · the relation itself is separately evidenced · strength of the relation: 0.95 · differs in (stated by the tagger, no quotation): whether the difficulty is within one label or between labels · the relation stated: the two named sources of detection difficulty - variation among instances sharing a label, and the sheer number of labels to be told apart
Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span adds the number of object categories to intraclass variations as a second challenge; distinct-from is loose. also in: intraclass variations · object categories | concept_edges[2] | 0.95 | the tagger's words |
| concept_edges | specific instances —distinct-from→ broad categories Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. differs in: whether the target is one object or a class of them · the relation itself is separately evidenced · strength of the relation: 0.95 · differs in (stated by the tagger, no quotation): whether the target is one object or a class of them · the relation stated: detecting one particular object is essentially a matching problem; detecting a category is the generic task this survey is about also in: specific instances · broad categories | concept_edges[3] | 0.95 | the tagger's words |
| concept_edges | accuracy —distinct-from→ efficiency Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. differs in: whether the constraint comes from the vision problem or from the deployment device · the relation itself is separately evidenced · strength of the relation: 0.95 · differs in (stated by the tagger, no quotation): whether the constraint comes from the vision problem or from the deployment device · the relation stated: the two competing goals - localising and recognising well enough to tell categories apart, against running in real time within a device's memory also in: accuracy · efficiency | concept_edges[4] | 0.95 | the tagger's words |
| content_tags | a detection counts as correct only if its overlap with the ground truth is not smaller than a predefined threshold Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | content_tags.findings[0] | 0.95 | the tagger's words |
| content_tags | MS COCO averages AP across IoU thresholds from 0.5 to 0.95 in steps of 0.05, instead of the traditional single IoU of 0.5. Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | content_tags.findings[1] | 0.95 | the tagger's words |
| content_tags | the benchmark class lists are 20, 200 and 91 categories, much smaller than what humans recognise Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | content_tags.findings[2] | 0.95 | the tagger's words |
| content_tags | one object class can span an astonishing variation in appearance while four near-identical images belong to four different classes Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | content_tags.findings[3] | 0.95 | the tagger's words |
| content_tags | size-stratified metrics were introduced because 41% of MS COCO objects are small and 24% are large Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | content_tags.findings[4] | 0.95 | the tagger's words |
| content_tags | the challenge winner is the team that wins the most object categories, with mAP justified as a team measure by the two rankings always agreeing Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | content_tags.findings[5] | 0.95 | the tagger's words |
| content_tags | high accuracy and high efficiency are named as competing goals of the same ideal algorithm Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | content_tags.findings[6] | 0.95 | the tagger's words |
| content_tags | a predicted detection whose category equals the ground-truth label and whose intersection-over-union with the ground-truth box is not smaller than a predefined threshold Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: True Positive | content_tags.definitions[0] | 0.95 | the tagger's words |
| content_tags | average precision computed per category and then averaged over all object categories, adopted as the final measure of performance Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: mAP | content_tags.definitions[1] | 0.95 | the tagger's words |
| content_tags | of the objects in MS COCO are small and 24% are large - the reason size-stratified metrics exist Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | content_tags.quantities[0] | 0.95 | the tagger's words |
| content_tags | more than this many research contributions are included in the survey Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. | content_tags.quantities[1] | 0.95 | the tagger's words |
| content_tags | mAP Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: mAP | content_tags.statistics[0] | 0.95 | the tagger's words |
| content_tags | IOU Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: IOU | content_tags.statistics[1] | 0.95 | the tagger's words |
| content_tags | object categories Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: object categories | content_tags.variables[0] | 0.95 | the tagger's words |
| content_tags | IOU Quotation withheld — this record is not cleared for redistribution. The sentence is in the paper at the link above. also in: IOU | content_tags.variables[1] | 0.95 | the tagger's words |
Tags that could not be placed in the text:
What was deliberately not tagged, and why, is recorded per paper by the tagger. Where a paper has no such record, this page says so rather than implying that nothing was declined.