Local Gains and Fixed-Assignment Set Losses in Shared Set Decoders
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Abstract (arXiv)
A query-relation deletion can improve the edited slot while reducing the utility of the prediction set that contains it. We study this tension in two related ResNet-50 DETR-family checkpoints using recorded, selection-conditional evidence from 710 paired image-relation units per checkpoint. The primary comparison subtracts a matched active control, which deletes the same leader source at a different recorded recipient, from the selected target deletion. It is therefore a composite contrast rather than a same-recipient placebo. The target-minus-control contrast is locally positive and fixed-assignment negative in both checkpoints. The opposite-sign pattern occurs within 302/710 DETR units and 460/710 DINO units. After rematching, the corresponding counts are 285/710 and 433/710. Rematching and native selection absorb enough of the mean loss for DETR intervals to cross zero, whereas DINO intervals remain negative, so persistence across readouts differs by checkpoint. A fixed-map comparison between hard deletion and a mass-preserving edit also differs before rematching. That comparison is conditional on the outcome-blind map and does not establish same-dose transport. Local intervention success therefore does not determine the consequence for a jointly decoded set. The supported conclusion is selection-conditional deletion sensitivity whose persistence depends on the readout and intervention operator. We do not identify an intervention-invariant edge mechanism, detector-level degradation, population prevalence, or the value of a training-time regularizer.
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References · 14
- 2020End-to-End Object Detection with Transformerscited by 7,384
- 2022DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detectioncited by 1,306
- 2019Attention is not Explanationcited by 916
- 2019Attention is not not Explanationcited by 629
- 2022DN-DETR: Accelerate DETR Training by Introducing Query DeNoisingcited by 486
- 2023Towards Best Practices of Activation Patching in Language Models: Metrics and Methodscited by 328
- 2022Group DETR: Fast DETR Training with Group-Wise One-to-Many Assignmentcited by 96
- 2023Is This the Subspace You Are Looking for? An Interpretability Illusion for Subspace Activation Patchingcited by 75
- 2023Detection Transformer with Stable Matchingcited by 43
- 2024Relation DETR: Exploring Explicit Position Relation Prior for Object Detectioncited by 27
- 2022Causal Abstraction with Soft Interventionscited by 23
- 2026Causality is Key for Interpretability Claims to Generalisecited by 8
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