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Paper Citation Record · LEDGER

$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization

As of 8 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2510.11296.

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pith.paper-citation-record.v1
2510.11296 v3

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measured 35 of 35 reference resolution

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measured 35 of 35 standing notices

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measured 0 of 0 inbound itemization

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35 of 35 outbound references displayed

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Outbound references

Observation dce7ccf4-1391-44a2-8903-bd5fd4120f06 · outbound

This paper cites In the table, OOD detection is measured by AUROC and FPR95 over 6 hard OOD detection datasets.

$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization In the table, OOD detection is measured by AUROC and FPR95 over 6 hard OOD detection datasets

Reference 1

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Observation a677f967-e728-40b8-bbff-ef4408194076 · outbound

This paper cites Therefore, unless otherwise specified, we set c= 2 in ∆Energyfor all experiments.

$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization Therefore, unless otherwise specified, we set c= 2 in ∆Energyfor all experiments

Reference 2

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Observation 0a7a8464-fdb8-4412-a8de-2dfd113088ed · outbound

This paper cites Conjugated Semantic Pool Improves OOD Detection with Pre-trained Vision-Language Models.

$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization Conjugated Semantic Pool Improves OOD Detection with Pre-trained Vision-Language Models

Reference 3

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Observation 4d3dbc82-218d-4ef4-bec8-049a794ffa4d · outbound

This paper cites A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks.

$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

Reference 6

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Observation a5e94870-c161-4887-9745-c6b55c482eb6 · outbound

This paper cites A detailed comparison of computation cost is provided in Table.

$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization A detailed comparison of computation cost is provided in Table

Reference 8

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Observation 2ca2bd87-a279-4835-8cf3-b9efcaaf5368 · outbound

This paper cites Negative Label Guided OOD Detection with Pretrained Vision-Language Models.

$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization Negative Label Guided OOD Detection with Pretrained Vision-Language Models

Reference 10

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Observation 02038908-1154-49e4-b7e6-6691de5f1393 · outbound

This paper cites WILDS: A Benchmark of in-the-Wild Distribution Shifts.

$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization WILDS: A Benchmark of in-the-Wild Distribution Shifts

Reference 11

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Observation e7fa1ec4-c1b4-45dc-a1e2-6c75f4e7f925 · outbound

This paper cites Recent Advances in Out-of-Distribution Detection with CLIP-Like Models: A Survey.

$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization Recent Advances in Out-of-Distribution Detection with CLIP-Like Models: A Survey

Reference 12

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This paper cites GL-MCM: Global and Local Maximum Concept Matching for Zero-Shot Out-of-Distribution Detection.

$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization GL-MCM: Global and Local Maximum Concept Matching for Zero-Shot Out-of-Distribution Detection

Reference 14

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Observation eef0db65-0c3f-42a9-ab27-2b3934d618fa · outbound

This paper cites Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey.

$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey

Reference 15

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Observation d5002f76-30bb-4d56-ae32-266281b9b7fa · outbound

This paper cites A Less Biased Evaluation of Out-of-distribution Sample Detectors.

$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization A Less Biased Evaluation of Out-of-distribution Sample Detectors

Reference 17

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Observation b23c9749-d1c5-42db-8875-5ac111532658 · outbound

This paper cites Towards Effective Semantic OOD Detection in Unseen Domains: A Domain Generalization Perspective.

$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization Towards Effective Semantic OOD Detection in Unseen Domains: A Domain Generalization Perspective

Reference 18

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This paper cites OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalization.

$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalization

Reference 19

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This paper cites Hybrid models for open set recognition.

$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization Hybrid models for open set recognition

Reference 20

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Observation 52bc2d64-fc76-4e3f-8c65-74ef080be272 · outbound

This paper cites Learning to Prompt for Vision-Language Models.

$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization Learning to Prompt for Vision-Language Models

Reference 22

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This paper cites DeCoOp: Robust Prompt Tuning with Out-of-Distribution Detection.

$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization DeCoOp: Robust Prompt Tuning with Out-of-Distribution Detection

Reference 23

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This paper cites CRoFT: Robust Fine-Tuning with Concurrent Optimization for OOD Generalization and Open-Set OOD Detection.

$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization CRoFT: Robust Fine-Tuning with Concurrent Optimization for OOD Generalization and Open-Set OOD Detection

Reference 24

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$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization Unresolved cited work

Reference 25

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This paper cites Prompt tuning methods aim to get better vision-language alignment via only fine-tuning the input prompts.

$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization Prompt tuning methods aim to get better vision-language alignment via only fine-tuning the input prompts

Reference 26

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Observation cda78688-33e4-4a45-96d2-0a1e306cc228 · outbound

This paper cites Adapter-tuning is another popular lightweight fine-tuning method, like CLIP-Adapter (Gao et al., 2023), Tip-Adapter (Zhang et al., 2021a).

$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization Adapter-tuning is another popular lightweight fine-tuning method, like CLIP-Adapter (Gao et al., 2023), Tip-Adapter (Zhang et al., 2021a)

Reference 27

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$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization Unresolved cited work

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$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization Unresolved cited work

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$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization 1 + es ˆy1 (x′)/τ −e ˜sˆy1 (x′)/τ P i̸=ˆy1 esi(x′)/τ +e ˜sˆy1 (x′)/τ # = log

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$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization end”, and set the class-specific context (CSC) as “False

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This paper cites This difference offers a novel approach to distinguishing between closed-set and open-set classes.

$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization This difference offers a novel approach to distinguishing between closed-set and open-set classes

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This paper cites VOS: Learning What You Don't Know by Virtual Outlier Synthesis.

$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization VOS: Learning What You Don't Know by Virtual Outlier Synthesis

Reference 2014

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This paper cites Scaling Out-of-Distribution Detection for Real-World Settings.

$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization Scaling Out-of-Distribution Detection for Real-World Settings

Reference 2016

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This paper cites Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks.

$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks

Reference 2017

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This paper cites Learning Transferable Visual Models From Natural Language Supervision.

$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization Learning Transferable Visual Models From Natural Language Supervision

Reference 2018

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This paper cites Aggregate-and-Adapt Natural Language Prompts for Downstream Generalization of CLIP.

$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization Aggregate-and-Adapt Natural Language Prompts for Downstream Generalization of CLIP

Reference 2019

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This paper cites OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detection.

$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detection

Reference 2020

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This paper cites Food-101–mining discriminative compo- nents with random forests.

$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization Food-101–mining discriminative compo- nents with random forests

Reference 2021

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This paper cites Dense outlier detection and open-set recognition based on training with noisy negative images.

$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization Dense outlier detection and open-set recognition based on training with noisy negative images

Reference 2022

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This paper cites In Search of Lost Domain Generalization.

$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization In Search of Lost Domain Generalization

Reference 2023

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Observation 890f41c0-a26b-4de1-a016-5442a921a842 · outbound

This paper cites Unsupervised Prompt Learning for Vision-Language Models.

$\Delta \mathrm{Energy}$: Optimizing Energy Change During Vision-Language Alignment Improves both OOD Detection and OOD Generalization Unsupervised Prompt Learning for Vision-Language Models

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