Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations | Paper Citation Record

Observation 711f6b55-66d8-4b88-a717-2556747a3ae3 · outbound

This paper cites Cosmos World Foundation Model Platform for Physical AI.

Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Cosmos World Foundation Model Platform for Physical AI

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Observation 7ed6ab5a-c8fa-434b-97f9-2a0326f97418 · outbound

This paper cites Diffusion visual counterfactual explana- tions.Advances in Neural Information Processing Systems, 35:364–377, 2022.

Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Diffusion visual counterfactual explana- tions.Advances in Neural Information Processing Systems, 35:364–377, 2022

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Observation 7e9d96f5-0739-485e-a7d0-1a14bc3a9b6e · outbound

This paper cites Dig-in: Diffusion guidance for investigating networks- uncovering classifier differences neuron visualisations and visual counterfactual explanations.

Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Dig-in: Diffusion guidance for investigating networks- uncovering classifier differences neuron visualisations and visual counterfactual explanations

Reference 3

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Observation c23f1a26-d6f7-4342-b7af-72b526e91c81 · outbound

This paper cites Sparse visual counterfac- tual explanations in image space.

Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Sparse visual counterfac- tual explanations in image space

Reference 4

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Observation 558b5dbb-fe08-4791-b8d4-6264da04744d · outbound

This paper cites Quo vadis, action recognition? a new model and the kinetics dataset.

Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Quo vadis, action recognition? a new model and the kinetics dataset

Reference 5

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Observation 680da456-9a73-4d8e-8bba-74dc3b8a7fd7 · outbound

This paper cites A frank-wolfe framework for efficient and effective adver- sarial attacks.

Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations A frank-wolfe framework for efficient and effective adver- sarial attacks

Reference 6

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Observation 6a155a07-438d-408b-8e5a-c180b5b5fd18 · outbound

This paper cites Learning temporal coherence via self- supervision for gan-based video generation.ACM Transac- tions on Graphics (TOG), 39(4):75–1, 2020.

Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Learning temporal coherence via self- supervision for gan-based video generation.ACM Transac- tions on Graphics (TOG), 39(4):75–1, 2020

Reference 7

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Observation fcf16372-cc04-429e-802b-a0f4b2332eb4 · outbound

This paper cites Diffusion models beat gans on image synthesis.Advances in neural informa- tion processing systems, 34:8780–8794, 2021.

Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Diffusion models beat gans on image synthesis.Advances in neural informa- tion processing systems, 34:8780–8794, 2021

Reference 8

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Observation b3a15108-faf1-40d1-9316-4dcf8fa32f08 · outbound

This paper cites Relative State.

Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Relative State

Reference 9

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Observation 8af35267-beb6-4ea5-8ecc-1443d657881c · outbound

This paper cites Robust physical-world attacks on deep learning visual classification.

Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Robust physical-world attacks on deep learning visual classification

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Observation a3750451-37dd-4e18-b974-abf86d1ba90a · outbound

This paper cites Latent Diffusion Counterfactual Explanations.

Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Latent Diffusion Counterfactual Explanations

Reference 11

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Observation 844a7516-7bd6-4260-9b34-45522688ee6f · outbound

This paper cites Tex- ture synthesis using convolutional neural networks.Ad- vances in neural information processing systems, 28, 2015.

Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Tex- ture synthesis using convolutional neural networks.Ad- vances in neural information processing systems, 28, 2015

Reference 12

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Observation 3dcc1618-c66c-4cf2-a1ae-36c1cc992ae5 · outbound

This paper cites A Neural Algorithm of Artistic Style.

Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations A Neural Algorithm of Artistic Style

Reference 13

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Observation 8647e0b2-a2a9-4aeb-a969-6bf82a432218 · outbound

This paper cites Counterfactual explanations and how to find them: literature review and benchmarking.Data Mining and Knowledge Discovery, 38(5):2770–2824, 2024.

Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Counterfactual explanations and how to find them: literature review and benchmarking.Data Mining and Knowledge Discovery, 38(5):2770–2824, 2024

Reference 14

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Observation 8d8aae57-05b3-40c5-9386-bb8d903467a7 · outbound

This paper cites Glide: a new approach for rapid, accurate dock- ing and scoring.

Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Glide: a new approach for rapid, accurate dock- ing and scoring

Reference 15

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Observation edde2a67-0edb-4638-9788-0ec6d98976ed · outbound

This paper cites Deep residual learning for image recognition.

Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Deep residual learning for image recognition

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Observation 61f4b6e2-a7b0-4043-952e-8e92ae676295 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilib- rium.Advances in neural information processing systems, 30, 2017.

Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Gans trained by a two time-scale update rule converge to a local nash equilib- rium.Advances in neural information processing systems, 30, 2017

Reference 17

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Observation 6923a810-b244-4edb-ab7b-adfc49309169 · outbound

This paper cites Denoising dif- fusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020.

Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Denoising dif- fusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

Reference 18

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Observation 0b52c5c4-9619-4053-afc7-0372adb0d470 · outbound

This paper cites An introduction to flow matching and diffusion models.arXiv preprint arXiv:2506.02070, 2025.

Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations An introduction to flow matching and diffusion models.arXiv preprint arXiv:2506.02070, 2025

Reference 19

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Observation 5d7525f6-c639-463e-9e05-ecff24315f4b · outbound

This paper cites Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022.

Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Lora: Low-rank adaptation of large language models.ICLR, 1(2):3, 2022

Reference 20

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Observation 7b257024-ca68-4f0e-8528-d32f2fa33c99 · outbound

This paper cites Steex: steering counter- factual explanations with semantics.

Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Steex: steering counter- factual explanations with semantics

Reference 21

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Observation 05081725-50aa-4ec5-96c3-7d4ade4dec95 · outbound

This paper cites Diffu- sion models for counterfactual explanations.

Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Diffu- sion models for counterfactual explanations

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Observation 7d61a9dc-4d56-4a2e-92c8-aa9d6aa9e9d4 · outbound

This paper cites Ad- versarial counterfactual visual explanations.

Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Ad- versarial counterfactual visual explanations

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Observation fc65f72c-6e45-411f-83db-ce96345df594 · outbound

This paper cites Text- to-image models for counterfactual explanations: a black- box approach.

Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Text- to-image models for counterfactual explanations: a black- box approach

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Observation 52db524d-5cd8-4c89-9e8a-8868e6e425d2 · outbound

This paper cites 3d convolu- tional neural networks for human action recognition.IEEE transactions on pattern analysis and machine intelligence, 35(1):221–231, 2012.

Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations 3d convolu- tional neural networks for human action recognition.IEEE transactions on pattern analysis and machine intelligence, 35(1):221–231, 2012

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This paper cites Multimodal explanations by predicting coun- terfactuality in videos.

Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Multimodal explanations by predicting coun- terfactuality in videos

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Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Text2video-zero: Text- to-image diffusion models are zero-shot video generators

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Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Cycle-consistent counter- factuals by latent transformations

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Observation 0ea10254-07b5-46c2-ba8d-2c53f061536c · outbound

This paper cites From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling.

Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling

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Observation 79ca9d28-9f9c-48b2-b3d3-ea0458dc9a7c · outbound

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Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations HunyuanVideo: A Systematic Framework For Large Video Generative Models

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This paper cites Imagenet classification with deep convolutional neural net- works.Advances in neural information processing systems, 25, 2012.

Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Imagenet classification with deep convolutional neural net- works.Advances in neural information processing systems, 25, 2012

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Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations On space-time interest points.International journal of computer vision, 64(2):107–123, 2005

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Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Flow Matching for Generative Modeling

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Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Structure matters: Tackling the semantic discrepancy in diffusion models for image inpainting

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Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Video swin transformer

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Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Zero-shot model diagnosis

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Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Towards Deep Learning Models Resistant to Adversarial Attacks

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Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Understanding the latent space of diffusion models through the lens of riemannian geometry

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Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Scalable diffusion models with transformers

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Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Learning transferable visual models from natural language supervi- sion

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Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations D’artagnan: Counterfactual video genera- tion

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Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Beyond trivial counterfactual explanations with diverse valuable explanations

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Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations High-resolution image synthesis with latent diffusion models

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Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Image synthesis with a single (robust) classifier.Advances in Neu- ral Information Processing Systems, 32, 2019

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Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Latent Diffusion Counterfactual Explanations.Springer, 15297 LNCS:295–311, 2025

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Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Ntu rgb+ d: A large scale dataset for 3d human activity anal- ysis

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Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Re- thinking visual counterfactual explanations through region constraint

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Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Denoising Diffusion Implicit Models

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Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Generative modeling by esti- mating gradients of the data distribution.Advances in neural information processing systems, 32, 2019

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Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Rethinking the inception archi- tecture for computer vision

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Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Towards Accurate Generative Models of Video: A New Metric & Challenges

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Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Understanding physical dynamics with counterfactual world modeling

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Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review

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Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Coun- terfactual explanations without opening the black box: Au- tomated decisions and the gdpr.Harv

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Observation a79fe48f-414f-4423-8e28-dd3cc9acd8ea · outbound

This paper cites Mead: A large-scale audio-visual dataset for emotional talking-face generation.

Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Mead: A large-scale audio-visual dataset for emotional talking-face generation

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-03T20:23:57.923881Z

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Observation d63fb565-70d1-449c-900d-8d2f6a4bec26 · outbound

This paper cites Video- to-video synthesis.

Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Video- to-video synthesis

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-03T20:23:57.927369Z

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Observation cb897eec-fca5-435c-8c4b-8547bdb78b6c · outbound

This paper cites Image quality assessment: from error visibility to structural similarity.IEEE transactions on image processing, 13(4):600–612, 2004.

Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Image quality assessment: from error visibility to structural similarity.IEEE transactions on image processing, 13(4):600–612, 2004

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-03T20:23:57.930813Z

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Observation ee9ee3d2-8df5-4bc4-b279-cbcb565c519a · outbound

This paper cites Fast diffusion-based counterfactuals for shortcut removal and generation.

Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Fast diffusion-based counterfactuals for shortcut removal and generation

Reference 60

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Observation 13f24a9e-cc9e-4caa-b97b-35c0284aec5d · outbound

This paper cites Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation.

Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation

Reference 61

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Observation 038f7d07-a8f2-4937-a558-0362e75da6f9 · outbound

This paper cites Celebv-text: A large-scale facial text-video dataset.

Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations Celebv-text: A large-scale facial text-video dataset

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-03T20:23:57.940646Z

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Observation a12fd937-21e0-44cc-ac76-a13a643b8ce5 · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptual metric.

Back to the Feature: Explaining Video Classifiers with Video Counterfactual Explanations The unreasonable effectiveness of deep features as a perceptual metric

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-03T20:23:57.944190Z

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