# Adaptive Temporal Encoding Network for Video Instance-level Human Parsing

arXiv: https://arxiv.org/abs/1808.00661 · HTML: https://mlsanity.com/paper/1808.00661/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2018.

## Built on by (most cited first, 30 of 36)

- [Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective](https://mlsanity.com/paper/2103.17263/) (2021)
- [Siamese Masked Autoencoders](https://mlsanity.com/paper/2305.14344/) (2023)
- [UniHCP: A Unified Model for Human-Centric Perceptions](https://mlsanity.com/paper/2303.02936/) (2023)
- [HumanBench: Towards General Human-centric Perception with Projector Assisted Pretraining](https://mlsanity.com/paper/2303.05675/) (2023)
- [Parsing is All You Need for Accurate Gait Recognition in the Wild](https://mlsanity.com/paper/2308.16739/) (2023)
- [Locality-Aware Inter-and Intra-Video Reconstruction for Self-Supervised Correspondence Learning](https://mlsanity.com/paper/2203.14333/) (2022)
- [Hulk: A Universal Knowledge Translator for Human-Centric Tasks](https://mlsanity.com/paper/2312.01697/) (2023)
- [Semantics Meets Temporal Correspondence: Self-supervised Object-centric Learning in Videos](https://mlsanity.com/paper/2308.09951/) (2023)
- [VideoMAC: Video Masked Autoencoders Meet ConvNets](https://mlsanity.com/paper/2402.19082/) (2024)
- [Deep Learning Technique for Human Parsing: A Survey and Outlook](https://mlsanity.com/paper/2301.00394/) (2023)
- [Efficient Image Pre-Training with Siamese Cropped Masked Autoencoders](https://mlsanity.com/paper/2403.17823/) (2024)
- [Modelling Neighbor Relation in Joint Space-Time Graph for Video Correspondence Learning](https://mlsanity.com/paper/2109.13499/) (2021)
- [Unified Mask Embedding and Correspondence Learning for Self-Supervised Video Segmentation](https://mlsanity.com/paper/2303.10100/) (2023)
- [AIParsing: Anchor-free Instance-level Human Parsing](https://mlsanity.com/paper/2207.06854/) (2022)
- [Semantic-Aware Fine-Grained Correspondence](https://mlsanity.com/paper/2207.10456/) (2022)
- [Spatial-then-Temporal Self-Supervised Learning for Video Correspondence](https://mlsanity.com/paper/2209.07778/) (2022)
- [Visual Representation Learning with Stochastic Frame Prediction](https://mlsanity.com/paper/2406.07398/) (2024)
- [Recurrent Video Masked Autoencoders](https://mlsanity.com/paper/2512.13684/) (2025)
- [StreamYOLO: Real-time Object Detection for Streaming Perception](https://mlsanity.com/paper/2207.10433/) (2022)
- [Transfer of Representations to Video Label Propagation: Implementation Factors Matter](https://mlsanity.com/paper/2203.05553/) (2022)
- [Image Comes Dancing with Collaborative Parsing-Flow Video Synthesis](https://mlsanity.com/paper/2110.14147/) (2021)
- [Smart Fashion: A Review of AI Applications in the Fashion & Apparel Industry](https://mlsanity.com/paper/2111.00905/) (2021)
- [Token Bottleneck: One Token to Remember Dynamics](https://mlsanity.com/paper/2507.06543/) (2025)
- [Parsing Objects at a Finer Granularity: A Survey](https://mlsanity.com/paper/2212.13693/) (2022)
- [FRAME: Pre-Training Video Feature Representations via Anticipation and Memory](https://mlsanity.com/paper/2506.05543/) (2025)
- [TIVE: A Toolbox for Identifying Video Instance Segmentation Errors](https://mlsanity.com/paper/2210.08856/) (2022)
- [Deep Learning for Human Parsing: A Survey](https://mlsanity.com/paper/2301.12416/) (2023)
- [Concatenated Masked Autoencoders as Spatial-Temporal Learner](https://mlsanity.com/paper/2311.00961/) (2023)
- [Self-Supervised Learning Based on Transformed Image Reconstruction for Equivariance-Coherent Feature Representation](https://mlsanity.com/paper/2503.18753/) (2025)
- [Training-Free Composed Video Retrieval via Visual Representation-Guided Video-LLM Reasoning](https://mlsanity.com/paper/2606.02321/) (2026)

## Abstract (arXiv)

Beyond the existing single-person and multiple-person human parsing tasks in static images, this paper makes the first attempt to investigate a more realistic video instance-level human parsing that simultaneously segments out each person instance and parses each instance into more fine-grained parts (e.g., head, leg, dress). We introduce a novel Adaptive Temporal Encoding Network (ATEN) that alternatively performs temporal encoding among key frames and flow-guided feature propagation from other consecutive frames between two key frames. Specifically, ATEN first incorporates a Parsing-RCNN to produce the instance-level parsing result for each key frame, which integrates both the global human parsing and instance-level human segmentation into a unified model. To balance between accuracy and efficiency, the flow-guided feature propagation is used to directly parse consecutive frames according to their identified temporal consistency with key frames. On the other hand, ATEN leverages the convolution gated recurrent units (convGRU) to exploit temporal changes over a series of key frames, which are further used to facilitate the frame-level instance-level parsing. By alternatively performing direct feature propagation between consistent frames and temporal encoding network among key frames, our ATEN achieves a good balance between frame-level accuracy and time efficiency, which is a common crucial problem in video object segmentation research. To demonstrate the superiority of our ATEN, extensive experiments are conducted on the most popular video segmentation benchmark (DAVIS) and a newly collected Video Instance-level Parsing (VIP) dataset, which is the first video instance-level human parsing dataset comprised of 404 sequences and over 20k frames with instance-level and pixel-wise annotations.