Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition | Paper Citation Record

Observation ee69c092-f209-4729-8774-6571b583892c · outbound

This paper cites The youtube video recommendation system,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition The youtube video recommendation system,

Reference 1

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Observation 17bcd798-c462-4af3-89fb-80a3201f2bf6 · outbound

This paper cites Content-based video recommendation system based on stylistic visual features,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Content-based video recommendation system based on stylistic visual features,

Reference 2

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Observation 67240e45-3801-4fb0-9f43-98eea11f0440 · outbound

This paper cites A unified personalized video recommendation via dynamic recurrent neural networks,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition A unified personalized video recommendation via dynamic recurrent neural networks,

Reference 3

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Observation 2ff0a395-4dff-4fd0-9414-73786b06b464 · outbound

This paper cites A system for video surveillance and monitoring,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition A system for video surveillance and monitoring,

Reference 4

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Observation 59c75ff5-ac8d-4d53-b7dd-4713190f6af2 · outbound

This paper cites Distributed deep learning model for intelligent video surveillance systems with edge computing,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Distributed deep learning model for intelligent video surveillance systems with edge computing,

Reference 5

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Observation 83530c02-eb62-4c4f-82f5-49b2d2a27214 · outbound

This paper cites Searching video for complex activities with finite state models,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Searching video for complex activities with finite state models,

Reference 6

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Observation 86c690d0-4c2f-4ffd-8ddd-fec1fd8983c6 · outbound

This paper cites Slowfast networks for video recognition,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Slowfast networks for video recognition,

Reference 7

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Observation 7024496e-9777-4bab-b8a2-3a4ae916ed7d · outbound

This paper cites Deep feature flow for video recognition,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Deep feature flow for video recognition,

Reference 8

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Observation 16464f36-6bf5-43c8-bb8c-587e955b8111 · outbound

This paper cites Convolutional two- stream network fusion for video action recognition,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Convolutional two- stream network fusion for video action recognition,

Reference 9

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Observation 32581267-a390-4312-b0ca-5b935d540ceb · outbound

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

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Quo vadis, action recognition? a new model and the kinetics dataset,

Reference 10

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Observation a5a8d62a-58aa-41bd-9999-5a17e0ad583a · outbound

This paper cites Learn- ing spatiotemporal features with 3d convolutional networks,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Learn- ing spatiotemporal features with 3d convolutional networks,

Reference 11

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Observation 7f7009b6-d49b-401b-8cce-c04ea8d02f55 · outbound

This paper cites Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet?.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet?

Reference 12

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Observation 2ba222c3-d250-462f-a094-b44e0e486e27 · outbound

This paper cites Liteeval: A coarse- to-fine framework for resource efficient video recognition,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Liteeval: A coarse- to-fine framework for resource efficient video recognition,

Reference 13

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Observation 8199ae95-3bdc-469f-aad4-18b4ab80d9f0 · outbound

This paper cites Multi-agent rein- forcement learning based frame sampling for effective untrimmed video recognition,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Multi-agent rein- forcement learning based frame sampling for effective untrimmed video recognition,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T15:13:04.084277Z

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Observation ba5d9e32-4134-41d1-a0a7-09876e9b7bec · outbound

This paper cites A dynamic frame selection framework for fast video recognition,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition A dynamic frame selection framework for fast video recognition,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T15:13:04.088611Z

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Observation edd45991-434d-43ac-90d7-5ea7f78620c6 · outbound

This paper cites Scsampler: Sampling salient clips from video for efficient action recognition,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Scsampler: Sampling salient clips from video for efficient action recognition,

Reference 16

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Observation 49073e31-c6d9-45e3-85fb-f407bb1482b3 · outbound

This paper cites Listen to look: Action recognition by previewing audio,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Listen to look: Action recognition by previewing audio,

Reference 17

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Observation 257b296e-694c-4f25-abdc-ec69ed0362d5 · outbound

This paper cites Ar-net: Adaptive frame resolution for efficient action recognition,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Ar-net: Adaptive frame resolution for efficient action recognition,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T15:13:04.101259Z

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Observation f9ede248-d690-4760-9659-f7097834426b · outbound

This paper cites Ocsampler: Compressing videos to one clip with single-step sampling,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Ocsampler: Compressing videos to one clip with single-step sampling,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T15:13:04.105683Z

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Observation 887306bf-c609-4b3d-b0b2-1f25b394a0d0 · outbound

This paper cites Recurrent models of visual attention,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Recurrent models of visual attention,

Reference 20

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Observation 6877945c-2c95-49bb-a5b0-854dd9065392 · outbound

This paper cites Look closer to see better: Recurrent attention convolutional neural network for fine-grained image recognition,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Look closer to see better: Recurrent attention convolutional neural network for fine-grained image recognition,

Reference 21

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Observation a186cd7c-45b9-4cf3-a441-a20fe5fe3d07 · outbound

This paper cites Spatially adaptive inference with stochastic feature sampling and interpolation,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Spatially adaptive inference with stochastic feature sampling and interpolation,

Reference 22

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Observation b4259aeb-c9fc-49cd-b53c-9aa11e3d1e91 · outbound

This paper cites Dynamic neural networks: A survey,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Dynamic neural networks: A survey,

Reference 23

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Observation ea90a582-9d19-4f04-9bc0-73f817c5a28c · outbound

This paper cites Glance and focus networks for dynamic visual recognition,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Glance and focus networks for dynamic visual recognition,

Reference 24

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Observation 76476764-0631-4c62-9de0-177d5781d2ad · outbound

This paper cites Dynamic spatial sparsification for efficient vision transformers and convolutional neural networks,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Dynamic spatial sparsification for efficient vision transformers and convolutional neural networks,

Reference 25

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Observation 02b5e970-70d8-452c-bd3e-b3e152f94e0d · outbound

This paper cites Tsm: Temporal shift module for efficient video understanding,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Tsm: Temporal shift module for efficient video understanding,

Reference 26

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Observation 5c53cc6d-1a4a-493b-8690-49dd92f280fe · outbound

This paper cites X3d: Expanding architectures for efficient video recognition,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition X3d: Expanding architectures for efficient video recognition,

Reference 27

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Observation 0606d5ed-99e5-4ffb-8c48-f9cb14bdea02 · outbound

This paper cites Adaptive focus for efficient video recognition,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Adaptive focus for efficient video recognition,

Reference 28

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Observation 92bc39ed-a569-43b9-ac0a-ba6792315a0d · outbound

This paper cites Adafocus v2: End-to-end training of spatial dynamic networks for video recognition,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Adafocus v2: End-to-end training of spatial dynamic networks for video recognition,

Reference 29

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Observation e51c1fa9-4a6a-4c7b-b3bd-20967d4dc7c3 · outbound

This paper cites Adafocusv3: On unified spatial-temporal dynamic video recognition,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Adafocusv3: On unified spatial-temporal dynamic video recognition,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T15:13:04.149600Z

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Observation b761353c-05ad-4df6-b27e-a68e188593c9 · outbound

This paper cites Activitynet: A large-scale video benchmark for human activity understanding,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Activitynet: A large-scale video benchmark for human activity understanding,

Reference 31

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Observation 120537fc-d2dd-450d-a60f-c2ea9ecbb57f · outbound

This paper cites Exploiting feature and class relationships in video categorization with regularized deep neural networks,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Exploiting feature and class relationships in video categorization with regularized deep neural networks,

Reference 32

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Observation e455f3ef-f987-41d7-9f7b-dca5c9950f0f · outbound

This paper cites The Kinetics Human Action Video Dataset.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition The Kinetics Human Action Video Dataset

Reference 33

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Observation ebc5312d-c7bf-40c3-814a-b48c901c2d24 · outbound

This paper cites The "something something.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition The "something something

Reference 34

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Observation 942d1591-27bf-468f-8a05-3665b83e0d88 · outbound

This paper cites The jester dataset: A large-scale video dataset of human gestures,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition The jester dataset: A large-scale video dataset of human gestures,

Reference 35

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Observation 1fe5fd39-bb1a-4fc7-8b0e-d6352305c9b0 · outbound

This paper cites Temporal segment networks: Towards good prac- tices for deep action recognition,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Temporal segment networks: Towards good prac- tices for deep action recognition,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-11T15:13:04.173782Z

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Observation b595a528-1fef-4f49-83a7-cdc5e92a5bc7 · outbound

This paper cites Long-term recurrent convolutional networks for visual recognition and description,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Long-term recurrent convolutional networks for visual recognition and description,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-11T15:13:04.178055Z

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Observation 1e1a6e66-c2d8-453e-b60c-d30bf93fd49c · outbound

This paper cites Recurrent tubelet proposal and recognition networks for action detection,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Recurrent tubelet proposal and recognition networks for action detection,

Reference 38

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Observation 25ce5452-f641-4535-a0dd-fae92b32d3a6 · outbound

This paper cites Beyond short snippets: Deep net- works for video classification,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Beyond short snippets: Deep net- works for video classification,

Reference 39

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Observation 10d6a03c-31e4-4b78-a312-e94414bcb166 · outbound

This paper cites Gate-shift networks for video action recognition,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Gate-shift networks for video action recognition,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-11T15:13:04.188575Z

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Observation 8828fd6b-00af-447d-8596-0639b840ac28 · outbound

This paper cites Adafuse: Adaptive temporal fusion network for efficient action recognition,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Adafuse: Adaptive temporal fusion network for efficient action recognition,

Reference 41

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Observation 9e4420a3-fe93-4e36-b275-be81ddb3abc1 · outbound

This paper cites Spatiotemporal multiplier networks for video action recognition,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Spatiotemporal multiplier networks for video action recognition,

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-11T15:13:04.194866Z

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Observation be1c474c-5a90-4935-8fc6-ec395d917566 · outbound

This paper cites Searching for two-stream models in multivariate space for video recognition,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Searching for two-stream models in multivariate space for video recognition,

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-11T15:13:04.199621Z

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Observation 34b8a736-7dd7-4f92-a13b-2606591972d9 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 44

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Observation 05a33649-efbe-4913-b24c-da2faab3fbc0 · outbound

This paper cites Vivit: A video vision transformer,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Vivit: A video vision transformer,

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-11T15:13:04.206428Z

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Observation 08fa2961-c0fc-4bdc-a6dc-dbd2eae4f743 · outbound

This paper cites Video swin transformer,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Video swin transformer,

Reference 46

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Observation 6e776662-972a-4abd-a459-fa8e758658ac · outbound

This paper cites Is space-time attention all you need for video understanding?.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Is space-time attention all you need for video understanding?

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-11T15:13:04.212962Z

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Observation 7768dda2-03df-4d8a-8c79-a8332b67f6ba · outbound

This paper cites Video transformer network,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Video transformer network,

Reference 48

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Observation 7cb709c0-f1c8-4017-a6ce-4b1fdafc4eb3 · outbound

This paper cites A closer look at spatiotemporal convolutions for action recognition,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition A closer look at spatiotemporal convolutions for action recognition,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:13:05.385600Z

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Observation 94c004c4-5215-415a-8442-7756795afd7e · outbound

This paper cites Eco: Efficient convolutional network for online video understanding,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Eco: Efficient convolutional network for online video understanding,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:13:05.375690Z

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Observation f9203d4d-3e9d-43db-8b55-b04b32dd55bd · outbound

This paper cites Video classification with channel-separated convolutional networks,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Video classification with channel-separated convolutional networks,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:13:05.366258Z

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Observation db7395b3-b2d0-4a75-9300-74681fc093a8 · outbound

This paper cites Teinet: Towards an efficient architecture for video recognition,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Teinet: Towards an efficient architecture for video recognition,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:13:05.355933Z

Source-reported events for the cited work

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Observation ab8fb1eb-aaed-4173-a47b-080305fb9d9b · outbound

This paper cites Tam: Temporal adaptive module for video recognition,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Tam: Temporal adaptive module for video recognition,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:13:05.344046Z

Source-reported events for the cited work

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Observation 9d8b70aa-d7ad-4090-b089-7fab46b318b2 · outbound

This paper cites End-to-end learning of action detection from frame glimpses in videos,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition End-to-end learning of action detection from frame glimpses in videos,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:13:05.333333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d1cfdb0c-40c8-4c77-9bf0-6576bb259b2f · outbound

This paper cites Frameexit: Condi- tional early exiting for efficient video recognition,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Frameexit: Condi- tional early exiting for efficient video recognition,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:13:05.323804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b538b53a-235c-4e02-87c5-c031039a6e05 · outbound

This paper cites Efficient action recognition via dynamic knowledge propagation,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Efficient action recognition via dynamic knowledge propagation,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:13:05.314908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9320492a-49fc-4ff4-bb44-43d65e5d73e0 · outbound

This paper cites Dynamic network quantization for efficient video inference,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Dynamic network quantization for efficient video inference,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:13:05.304842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c2b22354-e7b6-427d-ade5-d114be391777 · outbound

This paper cites Nsnet: Non-saliency suppression sampler for effi- cient video recognition,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Nsnet: Non-saliency suppression sampler for effi- cient video recognition,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:13:05.293908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 64728f50-fda2-4be9-85f8-017da047ad2b · outbound

This paper cites Temporal saliency query network for efficient video recognition,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Temporal saliency query network for efficient video recognition,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:13:05.283479Z

Source-reported events for the cited work

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Observation 5e59602a-a196-4ce0-ad13-999b47658cf9 · outbound

This paper cites Spatial trans- former networks,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Spatial trans- former networks,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:13:05.272484Z

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Observation 7c636865-86e3-4bbf-8a67-0f61da231e66 · outbound

This paper cites Sbnet: Sparse blocks network for fast inference,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Sbnet: Sparse blocks network for fast inference,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:13:05.261455Z

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Observation 43488e94-0c21-4556-b67d-e356c76833a8 · outbound

This paper cites Resolution adaptive networks for efficient inference,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Resolution adaptive networks for efficient inference,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:13:05.250627Z

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Observation ac7eb3e9-7623-49bf-ac92-6ffe57f6aebe · outbound

This paper cites Adaptively connected neural networks,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Adaptively connected neural networks,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:13:05.239658Z

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Observation 5f6ed2eb-d692-4a4b-8aaa-61d80bafc759 · outbound

This paper cites Dynamic region- aware convolution,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Dynamic region- aware convolution,

Reference 64

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Observation 15c89fd9-e7da-4407-86ea-4c19f35d844d · outbound

This paper cites Spatially adaptive computation time for residual networks,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Spatially adaptive computation time for residual networks,

Reference 65

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This paper cites Dynamic convolutions: Exploiting spatial sparsity for faster inference,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Dynamic convolutions: Exploiting spatial sparsity for faster inference,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:13:05.205905Z

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Observation d9f22a98-774c-4bdf-8eb9-29654f688a18 · outbound

This paper cites Interpretable spatio-temporal attention for video action recognition,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Interpretable spatio-temporal attention for video action recognition,

Reference 67

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Observation a064f1f9-101b-4dcf-9ea8-ddb1d1d33fa6 · outbound

This paper cites Generalized domain conditioned adaptation network,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Generalized domain conditioned adaptation network,

Reference 68

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Observation 92c099b4-b884-49f6-add0-b579a19a25ea · outbound

This paper cites Learning deep features for discriminative localization,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Learning deep features for discriminative localization,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:13:05.176146Z

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Observation 3194015d-0d86-4322-93d1-79422d588c34 · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Grad-cam: Visual explanations from deep networks via gradient-based localization,

Reference 70

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This paper cites An image is worth 16x16 words: Transformers for image recognition at scale,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 71

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Observation d62046a7-0547-40ea-b229-d2223e67c9e7 · outbound

This paper cites Delving into details: Synopsis-to-detail networks for video recognition,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Delving into details: Synopsis-to-detail networks for video recognition,

Reference 72

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This paper cites Long short-term memory,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Long short-term memory,

Reference 73

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Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Learning phrase rep- resentations using RNN encoder–decoder for statistical machine translation,

Reference 74

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Observation 0afd22a6-34b7-4fac-aa20-80060df2f2c3 · outbound

This paper cites Playing Atari with Deep Reinforcement Learning.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Playing Atari with Deep Reinforcement Learning

Reference 75

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Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Proximal Policy Optimization Algorithms

Reference 76

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Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Deep residual learning for image recognition,

Reference 77

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Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Convolutional networks with dense connectivity,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:13:05.113534Z

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Observation 82ff5d30-d05a-4fc0-9f7a-606bc3c9c6b4 · outbound

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Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Better mixing via deep representations,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:13:05.102735Z

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Observation 517f8c69-2d69-4873-be59-edc758c0f6cc · outbound

This paper cites Implicit semantic data augmentation for deep networks,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Implicit semantic data augmentation for deep networks,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:13:05.092070Z

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Observation 4776ceca-c4b8-4e76-bcd2-1c24c4d7bba5 · outbound

This paper cites Deep residual correction network for partial domain adaptation,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Deep residual correction network for partial domain adaptation,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:13:05.080764Z

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Observation 7e5b665c-6509-49eb-9abf-3c64154fbba8 · outbound

This paper cites Sepico: Semantic-guided pixel contrast for domain adaptive semantic segmentation,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Sepico: Semantic-guided pixel contrast for domain adaptive semantic segmentation,

Reference 82

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This paper cites Adapting across domains via target-oriented transferable semantic augmentation under prototype constraint,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Adapting across domains via target-oriented transferable semantic augmentation under prototype constraint,

Reference 83

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Observation 0c5d241c-6e87-4ce1-a4f8-e8a0e9515ce4 · outbound

This paper cites Multi-scale dense networks for resource efficient image classification,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Multi-scale dense networks for resource efficient image classification,

Reference 84

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Observation d53ece80-2b01-4f67-a71f-a8f13cd84088 · outbound

This paper cites Glance and focus: a dynamic approach to reducing spatial redundancy in image classification,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Glance and focus: a dynamic approach to reducing spatial redundancy in image classification,

Reference 85

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Observation c21710bf-78f6-4d6f-8d20-6a7e60537d3d · outbound

This paper cites Not all images are worth 16x16 words: Dynamic transformers for efficient image recognition,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Not all images are worth 16x16 words: Dynamic transformers for efficient image recognition,

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:13:05.029953Z

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This paper cites Watching a small portion could be as good as watching all: Towards efficient video classification,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Watching a small portion could be as good as watching all: Towards efficient video classification,

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:13:05.016447Z

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This paper cites Adamml: Adaptive multi-modal learning for efficient video recognition,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Adamml: Adaptive multi-modal learning for efficient video recognition,

Reference 88

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Observation 46d796a2-edda-468c-83d4-b9a83a4a737f · outbound

This paper cites Smart frame selection for action recognition,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Smart frame selection for action recognition,

Reference 89

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Observation c2f84d8e-2e40-4bdf-893e-830444a471be · outbound

This paper cites Look more but care less in video recognition,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Look more but care less in video recognition,

Reference 90

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This paper cites Resound: Towards action recognition without representation bias,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Resound: Towards action recognition without representation bias,

Reference 91

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Reference 92

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Reference 93

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Reference 94

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This paper cites Violence recognition from videos using deep learning techniques,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Violence recognition from videos using deep learning techniques,

Reference 95

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Observation 344b11ca-7f2e-4945-9eea-bf8b2e3e0435 · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Mobilenetv2: Inverted residuals and linear bottlenecks,

Reference 96

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Observation fc8991ea-db14-40b9-9ed9-ad18ebd786d9 · outbound

This paper cites Videos as space-time region graphs,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Videos as space-time region graphs,

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:13:04.905990Z

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Observation 29bd6d74-d020-4fdc-a3de-ac17194ec803 · outbound

This paper cites Temporal relational reasoning in videos,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Temporal relational reasoning in videos,

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:13:04.895284Z

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Observation cecfdc96-184a-464e-8c20-5aaeb8251154 · outbound

This paper cites Smallbignet: Integrating core and contextual views for video classification,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition Smallbignet: Integrating core and contextual views for video classification,

Reference 99

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Observation 55acfacc-b213-4858-8360-23cb98f3cc71 · outbound

This paper cites More is less: Learning efficient video representations by big-little network and depthwise temporal aggregation,.

Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition More is less: Learning efficient video representations by big-little network and depthwise temporal aggregation,

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:13:04.873079Z

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