FRAME: Pre-Training Video Feature Representations via Anticipation and Memory | Paper Citation Record

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This paper cites Self-supervised Object-Centric Learning for Videos.

FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Self-supervised Object-Centric Learning for Videos

Reference 1

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This paper cites Fully-Convolutional Siamese Networks for Object Tracking.

FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Fully-Convolutional Siamese Networks for Object Tracking

Reference 2

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FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Unresolved cited work

Reference 3

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This paper cites Emerging Properties in Self-Supervised Vision Transformers.

FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Emerging Properties in Self-Supervised Vision Transformers

Reference 4

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This paper cites A Simple Framework for Contrastive Learning of Visual Representations.

FRAME: Pre-Training Video Feature Representations via Anticipation and Memory A Simple Framework for Contrastive Learning of Visual Representations

Reference 5

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This paper cites Exploring Simple Siamese Representation Learning.

FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Exploring Simple Siamese Representation Learning

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This paper cites Tracking Anything with Decoupled Video Segmentation.

FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Tracking Anything with Decoupled Video Segmentation

Reference 8

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This paper cites Putting the Object Back into Video Object Segmentation.

FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Putting the Object Back into Video Object Segmentation

Reference 9

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FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Unresolved cited work

Reference 10

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This paper cites Doersch, A.

FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Doersch, A

Reference 11

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This paper cites Eymaël, R.

FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Eymaël, R

Reference 12

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This paper cites A Large-Scale Study on Unsupervised Spatiotemporal Representation Learning.

FRAME: Pre-Training Video Feature Representations via Anticipation and Memory A Large-Scale Study on Unsupervised Spatiotemporal Representation Learning

Reference 13

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This paper cites Grauman, A.

FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Grauman, A

Reference 14

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This paper cites Gupta, J.

FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Gupta, J

Reference 15

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This paper cites Momentum Contrast for Unsupervised Visual Representation Learning.

FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Momentum Contrast for Unsupervised Visual Representation Learning

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FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Unresolved cited work

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FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Unresolved cited work

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This paper cites Space-Time Correspondence as a Contrastive Random Walk.

FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Space-Time Correspondence as a Contrastive Random Walk

Reference 21

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This paper cites Jhuang, J.

FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Jhuang, J

Reference 22

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This paper cites CoTracker: It is Better to Track Together.

FRAME: Pre-Training Video Feature Representations via Anticipation and Memory CoTracker: It is Better to Track Together

Reference 23

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This paper cites The Kinetics Human Action Video Dataset.

FRAME: Pre-Training Video Feature Representations via Anticipation and Memory The Kinetics Human Action Video Dataset

Reference 24

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This paper cites Khosla, S.

FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Khosla, S

Reference 25

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This paper cites Segment Anything.

FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Segment Anything

Reference 26

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FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Kuehne, H

Reference 27

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FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Joint-task Self-supervised Learning for Temporal Correspondence

Reference 28

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FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Unresolved cited work

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FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Unresolved cited work

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FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Misra, C

Reference 31

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FRAME: Pre-Training Video Feature Representations via Anticipation and Memory DINOv2: Learning Robust Visual Features without Supervision

Reference 32

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This paper cites The 2017 DAVIS Challenge on Video Object Segmentation.

FRAME: Pre-Training Video Feature Representations via Anticipation and Memory The 2017 DAVIS Challenge on Video Object Segmentation

Reference 33

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FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Unresolved cited work

Reference 34

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FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Learning Transferable Visual Models From Natural Language Supervision

Reference 35

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FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Ranasinghe, M

Reference 36

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FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Ranzinger, G

Reference 37

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FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Fine-tuned CLIP Models are Efficient Video Learners

Reference 38

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FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Unresolved cited work

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FRAME: Pre-Training Video Feature Representations via Anticipation and Memory SAM 2: Segment Anything in Images and Videos

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FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Salehi, E

Reference 41

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FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Sameni, K

Reference 42

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FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Time-Contrastive Networks: Self-Supervised Learning from Video

Reference 43

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FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Region-Based Representations Revisited

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FRAME: Pre-Training Video Feature Representations via Anticipation and Memory UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 45

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FRAME: Pre-Training Video Feature Representations via Anticipation and Memory VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training

Reference 46

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FRAME: Pre-Training Video Feature Representations via Anticipation and Memory DINO-Tracker: Taming DINO for Self-Supervised Point Tracking in a Single Video

Reference 47

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FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Valmadre, L

Reference 48

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FRAME: Pre-Training Video Feature Representations via Anticipation and Memory ActionCLIP: A New Paradigm for Video Action Recognition

Reference 49

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FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Unresolved cited work

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FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Mask Propagation for Efficient Video Semantic Segmentation

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FRAME: Pre-Training Video Feature Representations via Anticipation and Memory What Should Not Be Contrastive in Contrastive Learning

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FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective

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FRAME: Pre-Training Video Feature Representations via Anticipation and Memory PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers

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FRAME: Pre-Training Video Feature Representations via Anticipation and Memory YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark

Reference 57

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FRAME: Pre-Training Video Feature Representations via Anticipation and Memory DVIS++: Improved Decoupled Framework for Universal Video Segmentation

Reference 58

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FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Adaptive Temporal Encoding Network for Video Instance-level Human Parsing

Reference 59

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FRAME: Pre-Training Video Feature Representations via Anticipation and Memory Masked Autoencoders Are Scalable Vision Learners

Reference 2021

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