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Making a Case for 3D Convolutions for Object Segmentation in Videos

https://doi.org/10.5244/c.34.65

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-01225-0_18openalex-snapshot

Spatio-temporal Channel Correlation Networks for Action Classification

inferredcites →https://doi.org/10.1007/978-3-030-01234-2_49openalex-snapshot

Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

inferredcites →https://doi.org/10.1007/978-3-030-01252-6_44openalex-snapshot

Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection

inferredcites →https://doi.org/10.1007/978-3-030-01267-0_19openalex-snapshot

Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification

inferredcites →https://doi.org/10.1007/978-3-030-58621-8_10openalex-snapshot

STEm-Seg: Spatio-Temporal Embeddings for Instance Segmentation in Videos

inferredcites →https://doi.org/10.1007/978-3-319-10593-2_42openalex-snapshot

Video Object Discovery and Co-segmentation with Extremely Weak Supervision

inferredcites →https://doi.org/10.1007/978-3-319-10602-1_48openalex-snapshot

Microsoft COCO: Common Objects in Context

inferredcites →https://doi.org/10.1007/978-3-319-46448-0_5openalex-snapshot

Learning to Refine Object Segments

inferredcites →https://doi.org/10.1109/cvpr.2007.382974openalex-snapshot

A Benchmark for the Comparison of 3-D Motion Segmentation Algorithms

inferredcites →https://doi.org/10.1109/cvpr.2009.5206848openalex-snapshot

ImageNet: A large-scale hierarchical image database

inferredcites →https://doi.org/10.1109/cvpr.2014.223openalex-snapshot

Large-Scale Video Classification with Convolutional Neural Networks

inferredcites →https://doi.org/10.1109/cvpr.2015.7298961openalex-snapshot

Saliency-aware geodesic video object segmentation

inferredcites →https://doi.org/10.1109/cvpr.2016.107openalex-snapshot

Track and Segment: An Iterative Unsupervised Approach for Video Object Proposals

inferredcites →https://doi.org/10.1109/cvpr.2016.85openalex-snapshot

A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation

inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot

Deep Residual Learning for Image Recognition

inferredcites →https://doi.org/10.1109/cvpr.2017.189openalex-snapshot

Large Kernel Matters — Improve Semantic Segmentation by Global Convolutional Network

inferredcites →https://doi.org/10.1109/cvpr.2017.195openalex-snapshot

Xception: Deep Learning with Depthwise Separable Convolutions

inferredcites →https://doi.org/10.1109/cvpr.2017.228openalex-snapshot

FusionSeg: Learning to Combine Motion and Appearance for Fully Automatic Segmentation of Generic Objects in Videos

inferredcites →https://doi.org/10.1109/cvpr.2017.502openalex-snapshot

Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset

inferredcites →https://doi.org/10.1109/cvpr.2017.565openalex-snapshot

One-Shot Video Object Segmentation

inferredcites →https://doi.org/10.1109/cvpr.2017.784openalex-snapshot

Primary Object Segmentation in Videos Based on Region Augmentation and Reduction

inferredcites →https://doi.org/10.1109/cvpr.2018.00342openalex-snapshot

Flow Guided Recurrent Neural Encoder for Video Salient Object Detection

inferredcites →https://doi.org/10.1109/cvpr.2018.00675openalex-snapshot

A Closer Look at Spatiotemporal Convolutions for Action Recognition

inferredcites →https://doi.org/10.1109/cvpr.2018.00685openalex-snapshot

Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?

inferredcites →https://doi.org/10.1109/cvpr.2018.00770openalex-snapshot

Fast Video Object Segmentation by Reference-Guided Mask Propagation

inferredcites →https://doi.org/10.1109/cvpr.2018.00813openalex-snapshot

Non-local Neural Networks

inferredcites →https://doi.org/10.1109/cvpr.2019.00813openalex-snapshot

MOTS: Multi-Object Tracking and Segmentation

inferredcites →https://doi.org/10.1109/cvpr.2019.01232openalex-snapshot

Large-Scale Weakly-Supervised Pre-Training for Video Action Recognition

inferredcites →https://doi.org/10.1109/iccv.2015.363openalex-snapshot

Unsupervised Object Discovery and Tracking in Video Collections

inferredcites →https://doi.org/10.1109/iccv.2015.510openalex-snapshot

Learning Spatiotemporal Features with 3D Convolutional Networks

inferredcites →https://doi.org/10.1109/iccv.2017.322openalex-snapshot

Mask R-CNN

inferredcites →https://doi.org/10.1109/iccv.2017.480openalex-snapshot

Learning Video Object Segmentation with Visual Memory

inferredcites →https://doi.org/10.1109/iccv.2019.00102openalex-snapshot

Anchor Diffusion for Unsupervised Video Object Segmentation

inferredcites →https://doi.org/10.1109/iccv.2019.00529openalex-snapshot

Video Instance Segmentation

inferredcites →https://doi.org/10.1109/iccv.2019.00565openalex-snapshot

Video Classification With Channel-Separated Convolutional Networks

inferredcites →https://doi.org/10.1109/icra.2019.8793683openalex-snapshot

Large-Scale Object Mining for Object Discovery from Unlabeled Video

inferredcites →https://doi.org/10.1109/icra.2019.8794254openalex-snapshot

Video Object Segmentation using Teacher-Student Adaptation in a Human Robot Interaction (HRI) Setting

inferredcites →https://doi.org/10.1109/tcsvt.2014.2308642openalex-snapshot

Superpixel-Based Spatiotemporal Saliency Detection

inferredcites →https://doi.org/10.1109/tcsvt.2016.2595324openalex-snapshot

Saliency Detection for Unconstrained Videos Using Superpixel-Level Graph and Spatiotemporal Propagation

inferredcites →https://doi.org/10.1109/tip.2014.2336549openalex-snapshot

Video Saliency Incorporating Spatiotemporal Cues and Uncertainty Weighting

inferredcites →https://doi.org/10.1109/tip.2015.2460013openalex-snapshot

Consistent Video Saliency Using Local Gradient Flow Optimization and Global Refinement

inferredcites →https://doi.org/10.1109/tip.2017.2754941openalex-snapshot

Video Salient Object Detection via Fully Convolutional Networks

inferredcites →https://doi.org/10.1109/tpami.2012.59openalex-snapshot

3D Convolutional Neural Networks for Human Action Recognition

inferredcites →https://doi.org/10.1109/tpami.2013.242openalex-snapshot

Segmentation of Moving Objects by Long Term Video Analysis

inferredcites →https://doi.org/10.4230/oasics.dx.2026.15openalex-snapshot

AutoResearch Lite: a reproducible paper-inspired experiment runner (Adam mechanism reproduction and convergence-speed comparison)

inferredcites →https://doi.org/10.48550/arxiv.1406.2199openalex-snapshot

Two-Stream Convolutional Networks for Action Recognition in Videos

inferredcites →https://doi.org/10.48550/arxiv.1506.04214openalex-snapshot

Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

inferredcites →https://doi.org/10.48550/arxiv.1604.04494openalex-snapshot

Long-term Temporal Convolutions for Action Recognition

inferredcites →https://doi.org/10.48550/arxiv.1701.05384openalex-snapshot

FusionSeg: Learning to combine motion and appearance for fully automatic segmention of generic objects in videos

inferredcites →https://doi.org/10.48550/arxiv.1705.06950openalex-snapshot

The Kinetics Human Action Video Dataset

inferredcites →https://doi.org/10.48550/arxiv.1706.05587openalex-snapshot

Rethinking Atrous Convolution for Semantic Image Segmentation

inferredcites →https://doi.org/10.48550/arxiv.1809.00461openalex-snapshot

YouTube-VOS: Sequence-to-Sequence Video Object Segmentation

inferredcites →https://doi.org/10.48550/arxiv.1907.08895openalex-snapshot

An Efficient 3D CNN for Action/Object Segmentation in Video

inferredcites →https://doi.org/10.5244/c.31.116openalex-snapshot

Online Adaptation of Convolutional Neural Networks for Video Object Segmentation

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-030-87237-3_62openalex-snapshot

Weakly-Supervised Ultrasound Video Segmentation with Minimal Annotations

inferred← citeshttps://doi.org/10.1007/978-981-95-0269-1_95openalex-snapshot

Machine Learning for Image and Video Analysis: Recent Advances and Challenges

inferred← citeshttps://doi.org/10.1007/s11263-023-01859-xopenalex-snapshot

The Right Spin: Learning Object Motion from Rotation-Compensated Flow Fields

inferred← citeshttps://doi.org/10.1007/s11263-024-02261-xopenalex-snapshot

Audio-Visual Segmentation with Semantics

inferred← citeshttps://doi.org/10.1016/j.compag.2024.108713openalex-snapshot

Monitoring mammalian herbivores via convolutional neural networks implemented on thermal UAV imagery

inferred← citeshttps://doi.org/10.1016/j.cviu.2024.103959openalex-snapshot

Survey on fast dense video segmentation techniques

inferred← citeshttps://doi.org/10.1016/j.cviu.2025.104460openalex-snapshot

SAVE: Segment Audio-Visual Easy way using the Segment Anything Model

inferred← citeshttps://doi.org/10.1016/j.heliyon.2024.e34845openalex-snapshot

Enhancing left ventricular segmentation in echocardiography with a modified mixed attention mechanism in SegFormer architecture

inferred← citeshttps://doi.org/10.1016/j.knosys.2025.113992openalex-snapshot

Progressive Confident Masking Attention Network for Audio–Visual Segmentation

inferred← citeshttps://doi.org/10.1016/j.media.2023.102751openalex-snapshot

FUN-SIS: A Fully UNsupervised approach for Surgical Instrument Segmentation

inferred← citeshttps://doi.org/10.1109/cvpr46437.2021.01655openalex-snapshot

Weakly Supervised Video Salient Object Detection

inferred← citeshttps://doi.org/10.1109/dicta52665.2021.9647227openalex-snapshot

Learning To Segment Dominant Object Motion From Watching Videos

inferred← citeshttps://doi.org/10.1109/elconrus51938.2021.9396226openalex-snapshot

Research of Neural Networks Efficiency in Video Generation Problem

inferred← citeshttps://doi.org/10.1109/iccv48922.2021.00159openalex-snapshot

Learning Motion-Appearance Co-Attention for Zero-Shot Video Object Segmentation

inferred← citeshttps://doi.org/10.1109/iccv48922.2021.00866openalex-snapshot

Deep Transport Network for Unsupervised Video Object Segmentation

inferred← citeshttps://doi.org/10.1109/iccvw69036.2025.00402openalex-snapshot

TransFlow: Motion Knowledge Transfer from Video Diffusion Models to Video Salient Object Detection

inferred← citeshttps://doi.org/10.1109/iccvw69036.2025.00404openalex-snapshot

DepthFlow: Exploiting Depth-Flow Structural Correlations for Unsupervised Video Object Segmentation

inferred← citeshttps://doi.org/10.1109/jbhi.2025.3629397openalex-snapshot

TD-SAM: Temporal and Distance-Guided Adaptations of SAM for Accurate Surgical Instrument Segmentation

inferred← citeshttps://doi.org/10.1109/lsp.2025.3586552openalex-snapshot

X-STA: Cross-Modal Spatial-Temporal Alignment Network for Unified Audio-Visual Segmentation

inferred← citeshttps://doi.org/10.1109/tcsvt.2025.3562186openalex-snapshot

Treating Motion as Option With Output Selection for Unsupervised Video Object Segmentation

inferred← citeshttps://doi.org/10.1109/tip.2025.3580269openalex-snapshot

Contrastive Conditional Latent Diffusion for Audio-Visual Segmentation

inferred← citeshttps://doi.org/10.1109/tmm.2026.3651123openalex-snapshot

Uncertainty-Aware Audio-Visual Segmentation With Dynamic Fusion for Multimodal Alignment

inferred← citeshttps://doi.org/10.1109/tpami.2021.3120228openalex-snapshot

Iterative Knowledge Exchange Between Deep Learning and Space-Time Spectral Clustering for Unsupervised Segmentation in Videos

inferred← citeshttps://doi.org/10.1109/wacv51458.2022.00198openalex-snapshot

Temporally stable video segmentation without video annotations

inferred← citeshttps://doi.org/10.1145/3565267openalex-snapshot

PAV-SOD: A New Task towards Panoramic Audiovisual Saliency Detection

inferred← citeshttps://doi.org/10.1145/3674225.3674394openalex-snapshot

Semi-AVS: Segmenting the Sounding Objects via Semi-supervised Learning

inferred← citeshttps://doi.org/10.1145/3735975openalex-snapshot

ALOHA: Adapting Local Spatio-Temporal Context to Enhance the Audio-Visual Semantic Segmentation

inferred← citeshttps://doi.org/10.1145/3746027.3755848openalex-snapshot

Shallow Features Matter: Hierarchical Memory with Heterogeneous Interaction for Unsupervised Video Object Segmentation

inferred← citeshttps://doi.org/10.1177/14780771251352965openalex-snapshot

ArchShapeNet: An interpretable 3D-CNN framework for evaluating architectural shapes

inferred← citeshttps://doi.org/10.70675/7b4980a9z109dz4ccez8cf5z0fef8b2c9cb6openalex-snapshot

Salient Object Segmentation in 360° images/videos and light field

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