Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning | Paper Citation Record

Observation 104cdbe5-829b-47c5-9318-83bc5fbfc750 · outbound

This paper cites Energy effi- cient uav-enabled mobile edge computing for iot devices: A review.IEEE Access, 9:127779–127798, 2021.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Energy effi- cient uav-enabled mobile edge computing for iot devices: A review.IEEE Access, 9:127779–127798, 2021

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:28:31.221647Z

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Observation fba90947-733d-45e8-9b93-f00a3ebb90ec · outbound

This paper cites Continual Test-Time Adaptation for Object Detection with Adaptive Monitoring and Randomized Restoration.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Continual Test-Time Adaptation for Object Detection with Adaptive Monitoring and Randomized Restoration

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:28:24.375947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 01292a2a-cc21-4398-9673-2b63e5edd2fa · outbound

This paper cites End-to- end object detection with transformers.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning End-to- end object detection with transformers

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T11:28:20.548613Z

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Observation 3001f664-4f6e-4c3e-973b-d0ca2efe9be0 · outbound

This paper cites STFAR: Improving Object Detection Robustness at Test-Time by Self-Training with Feature Alignment Regularization.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning STFAR: Improving Object Detection Robustness at Test-Time by Self-Training with Feature Alignment Regularization

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T11:28:20.638961Z

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Observation 093bbad3-f232-486a-811b-dd6e62e7f13a · outbound

This paper cites Exploiting low-confidence pseudo-labels for source-free object detec- tion.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Exploiting low-confidence pseudo-labels for source-free object detec- tion

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:28:31.008690Z

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Observation ce67d81c-2c21-466d-885b-e6a754186121 · outbound

This paper cites A survey on deep neural network pruning: Taxonomy, compar- ison, analysis, and recommendations.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning A survey on deep neural network pruning: Taxonomy, compar- ison, analysis, and recommendations.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:28:30.843232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 25e31952-3770-402f-815e-cb1e0fec9420 · outbound

This paper cites Adversarial alignment for source free object detection.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Adversarial alignment for source free object detection

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:28:30.682469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 462d9b81-1ee9-460b-af2b-b46d954bd0e4 · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning The cityscapes dataset for semantic urban scene understanding

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:28:30.442569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b8fdbdf6-5166-4b12-936b-e117d0e2cf35 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Imagenet: A large-scale hierarchical image database

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T11:28:21.415851Z

Source-reported events for the cited work

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Observation 993692ab-ebff-4e4a-ac54-6dd19ecd0840 · outbound

This paper cites Balanced teacher for source-free object detection.IEEE Transactions on Circuits and Systems for Video Technology, 2024.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Balanced teacher for source-free object detection.IEEE Transactions on Circuits and Systems for Video Technology, 2024

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:28:30.298199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e5eb48ef-cacb-4779-817c-b20bb8310796 · outbound

This paper cites Ob- ject detection using yolo: Challenges, architectural succes- sors, datasets and applications.multimedia Tools and Appli- cations, 82(6):9243–9275, 2023.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Ob- ject detection using yolo: Challenges, architectural succes- sors, datasets and applications.multimedia Tools and Appli- cations, 82(6):9243–9275, 2023

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:28:30.101376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 51fc8240-88ca-42b4-b8ce-001206bd1601 · outbound

This paper cites The unmanned aerial vehicle benchmark: Object detection and tracking.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning The unmanned aerial vehicle benchmark: Object detection and tracking

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:28:29.902158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 56dc6fb0-1096-408f-ac50-a9b851a5d5ec · outbound

This paper cites Dynamic Channel Pruning: Feature Boosting and Suppression.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Dynamic Channel Pruning: Feature Boosting and Suppression

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T11:28:22.043164Z

Source-reported events for the cited work

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Observation 229fe355-b9b6-42c2-82a8-01d9171eb616 · outbound

This paper cites Learning dual convolutional dictionaries for image de-raining.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Learning dual convolutional dictionaries for image de-raining

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:28:29.739771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 29c6dcca-1b24-4380-a83c-c37336789b4c · outbound

This paper cites Neuromorphic event signal-driven network for video de-raining.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Neuromorphic event signal-driven network for video de-raining

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:28:29.489852Z

Source-reported events for the cited work

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Observation 95142f63-af73-43e9-940d-c92a894505ff · outbound

This paper cites Deep residual learning for image recognition.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Deep residual learning for image recognition

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T11:28:22.472363Z

Source-reported events for the cited work

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Observation 4e6efd2b-7c26-4b2a-94e9-2026e67eaacd · outbound

This paper cites Mask r-cnn.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Mask r-cnn

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T11:28:22.493506Z

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Observation 05235505-e076-454f-8073-7787bce41849 · outbound

This paper cites Structured pruning for deep con- volutional neural networks: A survey.IEEE transactions on pattern analysis and machine intelligence, 2023.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Structured pruning for deep con- volutional neural networks: A survey.IEEE transactions on pattern analysis and machine intelligence, 2023

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:28:29.264283Z

Source-reported events for the cited work

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Observation 3ccd63ad-7094-4589-915c-cd4001a9217e · outbound

This paper cites Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T11:28:22.503519Z

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Observation 98104872-4b3d-4811-af56-09d2aab2410c · outbound

This paper cites Benchmarking Neural Network Robustness to Common Corruptions and Perturbations.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T11:28:22.507594Z

Source-reported events for the cited work

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Observation da3f7c36-6d69-4544-900b-d699dc48815e · outbound

This paper cites What’s the backward- forward flop ratio for neural networks?, 2021.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning What’s the backward- forward flop ratio for neural networks?, 2021

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:28:29.050639Z

Source-reported events for the cited work

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Observation 12247947-f913-4c63-8174-4d095e3f5311 · outbound

This paper cites Channel gating neural networks.Ad- vances in Neural Information Processing Systems, 32, 2019.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Channel gating neural networks.Ad- vances in Neural Information Processing Systems, 32, 2019

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:28:28.791190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 242f7710-aadf-4c12-b9ce-26554da8354b · outbound

This paper cites Dynamic Retraining-Updating Mean Teacher for Source-Free Object Detection.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Dynamic Retraining-Updating Mean Teacher for Source-Free Object Detection

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:28:24.235140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 23339796-c393-418c-bb90-d11ec10e428e · outbound

This paper cites Dynamic slimmable network.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Dynamic slimmable network

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:28:28.593475Z

Source-reported events for the cited work

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Observation 200143de-3e96-4efa-ab71-9d58c0f4f3ac · outbound

This paper cites A free lunch for unsuper- vised domain adaptive object detection without source data.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning A free lunch for unsuper- vised domain adaptive object detection without source data

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:28:28.323717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 04506c90-8e6c-4aab-bfcb-db3c9e78a4e4 · outbound

This paper cites Runtime neural pruning.Advances in neural information processing systems, 30, 2017.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Runtime neural pruning.Advances in neural information processing systems, 30, 2017

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:28:28.084677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 08f8964f-3536-4fac-a4e8-417cb9de9301 · outbound

This paper cites Periodically exchange teacher-student for source-free object detection.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Periodically exchange teacher-student for source-free object detection

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:28:27.880806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d194d91a-f3b9-4d5d-a338-8c7b398f3692 · outbound

This paper cites Edge computing for autonomous driving: Opportunities and challenges.Proceedings of the IEEE, 107 (8):1697–1716, 2019.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Edge computing for autonomous driving: Opportunities and challenges.Proceedings of the IEEE, 107 (8):1697–1716, 2019

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:28:27.658630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4d7aef13-438d-4654-a655-242739869a8b · outbound

This paper cites Learning efficient convolutional networks through network slimming.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Learning efficient convolutional networks through network slimming

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:28:27.442443Z

Source-reported events for the cited work

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Observation d9e6756c-ce79-403b-900c-62d8190e2a49 · outbound

This paper cites The norm must go on: Dynamic unsuper- vised domain adaptation by normalization.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning The norm must go on: Dynamic unsuper- vised domain adaptation by normalization

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:28:27.183762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4c143f51-2364-473a-8813-8620817e84dc · outbound

This paper cites Act- mad: Activation matching to align distributions for test- time-training.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Act- mad: Activation matching to align distributions for test- time-training

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:28:26.976025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fdb1f660-ea7a-43e7-baf0-d5b56ca3fa76 · outbound

This paper cites Faster r-cnn: Towards real-time object detection with region proposal networks.IEEE transactions on pattern analysis and machine intelligence, 39(6):1137–1149, 2016.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Faster r-cnn: Towards real-time object detection with region proposal networks.IEEE transactions on pattern analysis and machine intelligence, 39(6):1137–1149, 2016

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:28:26.785775Z

Source-reported events for the cited work

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Observation 6e59579f-4096-4d73-94c3-4ff233dd3f31 · outbound

This paper cites Fully test-time adaptation for object detection.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Fully test-time adaptation for object detection

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:28:26.534983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 99132640-3047-40a6-8e84-c2b70946c8d5 · outbound

This paper cites Acdc: The adverse conditions dataset with correspondences for se- mantic driving scene understanding.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Acdc: The adverse conditions dataset with correspondences for se- mantic driving scene understanding

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:28:26.326479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 55c1a22c-5e62-417b-894b-b5ee450af29a · outbound

This paper cites Test: Test-time self-training under distribution shift.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Test: Test-time self-training under distribution shift

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:28:26.124062Z

Source-reported events for the cited work

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Observation c3ae5720-280a-4f4b-9052-87316b515321 · outbound

This paper cites A comprehensive review of recent research trends on unmanned aerial vehicles (uavs).Systems, 11(8):400,.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning A comprehensive review of recent research trends on unmanned aerial vehicles (uavs).Systems, 11(8):400,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:28:26.026956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e6d933de-04e3-43fe-873d-8517759b1fb5 · outbound

This paper cites Object recognition and detection with deep learning for autonomous driving applications.Simulation, 93(9):759–769, 2017.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Object recognition and detection with deep learning for autonomous driving applications.Simulation, 93(9):759–769, 2017

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:28:25.903010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 485fa6c5-cac0-4281-9438-21d4cda48788 · outbound

This paper cites Instance rela- tion graph guided source-free domain adaptive object detec- tion.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Instance rela- tion graph guided source-free domain adaptive object detec- tion

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:28:25.762115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4f2c5df5-c651-416e-a6d6-c2ea697e3b82 · outbound

This paper cites Generalized uav object detec- tion via frequency domain disentanglement.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Generalized uav object detec- tion via frequency domain disentanglement

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:28:25.562543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation afc097a7-0173-41a2-9b52-2027012c93c8 · outbound

This paper cites Towards generalized uav object detection: A novel perspective from frequency domain disentanglement.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Towards generalized uav object detection: A novel perspective from frequency domain disentanglement

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:28:25.385249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f255706d-6cd2-4e15-9645-211ecb18fe04 · outbound

This paper cites Continual test-time domain adaptation.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Continual test-time domain adaptation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:28:25.137185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f9349e65-13ef-4dcc-a9bd-8c3cbfd7e90a · outbound

This paper cites an unresolved cited work.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Unresolved cited work

Reference 42

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:28:24.947515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 32447ea0-dec1-4b57-b126-c4e236130602 · outbound

This paper cites Enhancing Source-Free Domain Adaptive Object Detection with Low-confidence Pseudo Label Distillation.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Enhancing Source-Free Domain Adaptive Object Detection with Low-confidence Pseudo Label Distillation

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T11:28:24.053304Z

Source-reported events for the cited work

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Observation 4c3bdc5a-5676-4288-ac91-ec6c82d77f35 · outbound

This paper cites Gate decorator: Global filter pruning method for ac- celerating deep convolutional neural networks.Advances in neural information processing systems, 32, 2019.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Gate decorator: Global filter pruning method for ac- celerating deep convolutional neural networks.Advances in neural information processing systems, 32, 2019

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:28:24.747257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2f7aacd2-f165-401e-b6fd-c32fac31e4e9 · outbound

This paper cites Uni-NaVid: A Video-based Vision-Language-Action Model for Unifying Embodied Navigation Tasks.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Uni-NaVid: A Video-based Vision-Language-Action Model for Unifying Embodied Navigation Tasks

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T11:28:24.066688Z

Source-reported events for the cited work

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Observation c7f5d374-f723-442d-86db-3fa3466d9a78 · outbound

This paper cites NaVid: Video-based VLM Plans the Next Step for Vision-and-Language Navigation.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning NaVid: Video-based VLM Plans the Next Step for Vision-and-Language Navigation

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T11:28:24.074868Z

Source-reported events for the cited work

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Observation 5e533d3b-41f9-41a0-a308-54d181a6042c · outbound

This paper cites Multi-source-free domain adaptive object detection.International Journal of Computer Vision, pages 1–33, 2024.

Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning Multi-source-free domain adaptive object detection.International Journal of Computer Vision, pages 1–33, 2024

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:28:24.562726Z

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