Paper Citation Record · LEDGER

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss

As of 22 July 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2402.08267.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2402.08267 v3

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-24T03:44:45.843249Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-07-22T06:31:00.163083+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

34 of 34 outbound references displayed

verified exact2
verified fuzzy32
unresolved0
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malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 55d292e1-659f-45ad-b7ea-fc1517a00dee · outbound

This paper cites The scenarios in which machine analysis systems are utilized are generally classified into edge computing and cloud computing.

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss The scenarios in which machine analysis systems are utilized are generally classified into edge computing and cloud computing

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T03:45:59.086584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 94c9cba1-44cc-4e94-958d-a72dc977f893 · outbound

This paper cites an unresolved cited work.

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss Unresolved cited work

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T03:45:59.115180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation de2533ec-a2fe-446c-a61b-67d8a6aba1fa · outbound

This paper cites For this reason, many studies take task loss-based optimization [3-5] or ROI-based bit allocation approaches [8,9].

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss For this reason, many studies take task loss-based optimization [3-5] or ROI-based bit allocation approaches [8,9]

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T03:45:59.105647Z

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No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 9304d216-55a5-4cb7-9a32-e577bd23881d · outbound

This paper cites an unresolved cited work.

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss Unresolved cited work

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T03:45:59.126642Z

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Observation a9ca82ea-0ac2-4c86-bfb2-7130ad91349a · outbound

This paper cites As in the object detection task, we followed the training manner [ 5].

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss As in the object detection task, we followed the training manner [ 5]

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T03:45:59.120907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation d8ef890a-ff6b-4a99-92de-9a07216b9bc0 · outbound

This paper cites Our proposed method imposes the auxiliary loss on the encoder of a compression model via a lightweight recognition model during training.

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss Our proposed method imposes the auxiliary loss on the encoder of a compression model via a lightweight recognition model during training

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T03:45:59.111823Z

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No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 1066a358-7b06-4038-858c-b2ad6a302d5e · outbound

This paper cites High efficiency video coding.

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss High efficiency video coding

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T03:45:59.077716Z

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No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation dcfc940b-c65c-47db-9858-6bbe1d9fc712 · outbound

This paper cites Versatile video coding.

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss Versatile video coding

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T03:45:59.123521Z

Source-reported events for the cited work

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Observation e50d26b9-baae-4a22-bc59-bd3e5bb2909f · outbound

This paper cites Image coding fo r machines: an end -to-end learned approach.

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss Image coding fo r machines: an end -to-end learned approach

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T03:45:59.099018Z

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No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 7d82bef2-eed5-4846-9fe1-8d96d99a4e39 · outbound

This paper cites Rate -distortion in image ´ coding for machines.

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss Rate -distortion in image ´ coding for machines

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T03:45:59.108473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 97ed2dc7-bbbe-491c-9da5-f058e4d1dccb · outbound

This paper cites Deep Feature Compressio n using Rate -Distortion Optimization Guided Autoencoder.

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss Deep Feature Compressio n using Rate -Distortion Optimization Guided Autoencoder

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T03:45:59.089053Z

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No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 00e63fff-8b41-4e11-848b-41d461d5e403 · outbound

This paper cites Visual analysis motivated rate - distortion model for image coding.

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss Visual analysis motivated rate - distortion model for image coding

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T03:45:59.080571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation df4271a4-747e-4250-a919-b70feb6bc1c7 · outbound

This paper cites Choi and I.

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss Choi and I

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T03:45:59.160704Z

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No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation ecc60f09-18bf-4e67-8685-5ce04ee4301b · outbound

This paper cites Region of Interest Enabled Le arned Image Coding for Machines.

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss Region of Interest Enabled Le arned Image Coding for Machines

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T03:45:59.118326Z

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No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 21508310-5cc7-4b2f-82ca-9204c0776fc0 · outbound

This paper cites Region -of-interest and channel attent ion -based joint optimization of image compression and computer vision.

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss Region -of-interest and channel attent ion -based joint optimization of image compression and computer vision

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T03:45:59.179803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation cb48df91-11dc-42cb-b407-d3728958ee9e · outbound

This paper cites [VCM] On VCM reporting template.

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss [VCM] On VCM reporting template

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T03:45:59.182811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 948600df-c14f-4181-a316-b23bc3851770 · outbound

This paper cites Matsubara, R.

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss Matsubara, R

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T03:45:59.152221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 642cc076-c9bb-460a-85bb-27028a241988 · outbound

This paper cites Head Network Distillation: Splitting Distilled Deep Neural Networks for Resource-Constrained Edg e Computing Systems.

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss Head Network Distillation: Splitting Distilled Deep Neural Networks for Resource-Constrained Edg e Computing Systems

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T03:45:59.129809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 81f24741-d442-47ad-a5b2-27edf762863a · outbound

This paper cites BottleFit: Learni ng Compressed Representations in Deep Neural Networks for Effective and Efficient Sp lit Computing.

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss BottleFit: Learni ng Compressed Representations in Deep Neural Networks for Effective and Efficient Sp lit Computing

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T03:45:59.162950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 3b55e223-ad5f-4889-a598-17415af86ded · outbound

This paper cites Relay backpropagation for effective learning of deep c onvolutional neural networks.

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss Relay backpropagation for effective learning of deep c onvolutional neural networks

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T03:45:59.149687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation ea8b018c-95e1-4356-b3b2-b68904375ac2 · outbound

This paper cites Deeply supervised nets. 2015 International Conference on Artificial Intelligence and Statistics (AISTATS).

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss Deeply supervised nets. 2015 International Conference on Artificial Intelligence and Statistics (AISTATS)

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T03:45:59.155123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 1cc1ce28-e21c-46ab-92c9-7db45a8fd032 · outbound

This paper cites Learned Image Compression with Mixed Transformer -CNN Architectures.

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss Learned Image Compression with Mixed Transformer -CNN Architectures

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T03:45:59.157796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation a45ef1a1-aa6c-4449-903a-afb29b4748f8 · outbound

This paper cites Learned image compression with di scretized gaussian mixture likelihoods and attention modules.

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss Learned image compression with di scretized gaussian mixture likelihoods and attention modules

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T03:45:59.147072Z

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No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation da094b38-177b-4e9d-b9cf-6bbc18a27c10 · outbound

This paper cites Noise or signal: The role of image backgrounds in object recognition.

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss Noise or signal: The role of image backgrounds in object recognition

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T03:45:59.094279Z

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No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 12f8d0a7-bb2a-4b5f-8212-6dd7ec8974a6 · outbound

This paper cites CompressAI: a PyTorch library and evaluation platform for end-to-end compression research.

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss CompressAI: a PyTorch library and evaluation platform for end-to-end compression research

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-24T03:45:58.539587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 9ba37ffd-07af-47e1-814b-6a0c0ed796c1 · outbound

This paper cites Faster R-CNN: Towards Real- Time Objec t Detection with Region Proposal Networks.

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss Faster R-CNN: Towards Real- Time Objec t Detection with Region Proposal Networks

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T03:45:59.141668Z

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No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation d698560b-d16c-4e83-ade7-b38281e2dd3c · outbound

This paper cites Detectron2.

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss Detectron2

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T03:45:59.144458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 6fa43761-dc19-4809-96be-55be45654e5f · outbound

This paper cites Aggregated residual transformations for deep neural networks.

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss Aggregated residual transformations for deep neural networks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T03:45:59.083060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 11a5597c-30b9-4491-8d82-d3ace49a0fe8 · outbound

This paper cites an unresolved cited work.

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss Unresolved cited work

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T03:45:59.135298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 98515e19-6db6-4ad0-ae46-875b4131be23 · outbound

This paper cites Deep residual learning for image recognition.

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss Deep residual learning for image recognition

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T03:45:59.091816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 3b469dab-f16b-4c9a-9039-a61bacf2652e · outbound

This paper cites Microsoft COCO: Common Objects in Contex t.

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss Microsoft COCO: Common Objects in Contex t

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T03:45:59.102124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation d143567d-bb68-40f4-bcb7-1a5903ab0d8a · outbound

This paper cites OpenMMLab Semantic Segmentation Toolbox and Benchmark.

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss OpenMMLab Semantic Segmentation Toolbox and Benchmark

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T03:45:59.132565Z

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No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation f5ab79d4-efca-4711-9804-3d103b9e68b9 · outbound

This paper cites The Role of Context for Object Detection and Semantic Segmentation in the Wild.

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss The Role of Context for Object Detection and Semantic Segmentation in the Wild

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T03:45:59.138303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 76b2cee8-cc0e-463e-9b1f-ffa00e610efd · outbound

This paper cites Adapting Auxiliary Losses Using Gradient Similarity.

Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss Adapting Auxiliary Losses Using Gradient Similarity

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-24T03:45:58.545952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Pith citing papers

No inbound Pith citation observations are available.