# Neuron: Learning Context-Aware Evolving Representations for Zero-Shot Skeleton Action Recognition

arXiv: https://arxiv.org/abs/2411.11288 · HTML: https://mlsanity.com/paper/2411.11288/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 58 of its 98 references that are in the map.
- First posted on arXiv 18 Nov 2024.

## Builds on (most cited first, 30 of 58)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis](https://mlsanity.com/paper/1604.02808/) (2016)
- [NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding](https://mlsanity.com/paper/1905.04757/) (2019)
- [Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2107.12213/) (2021)
- [An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild](https://mlsanity.com/paper/1605.04253/) (2016)
- [PKU-MMD: A Large Scale Benchmark for Continuous Multi-Modal Human Action Understanding](https://mlsanity.com/paper/1703.07475/) (2017)
- [Fine-Grained Action Retrieval Through Multiple Parts-of-Speech Embeddings](https://mlsanity.com/paper/1908.03477/) (2019)
- [Beyond Question-Based Biases: Assessing Multimodal Shortcut Learning in Visual Question Answering](https://mlsanity.com/paper/2104.03149/) (2021)
- [Learning Discriminative Representations for Skeleton Based Action Recognition](https://mlsanity.com/paper/2303.03729/) (2023)
- [Learning Robust Visual-Semantic Embeddings](https://mlsanity.com/paper/1703.05908/) (2017)
- [Generative Action Description Prompts for Skeleton-based Action Recognition](https://mlsanity.com/paper/2208.05318/) (2022)
- [CMD: Self-supervised 3D Action Representation Learning with Cross-modal Mutual Distillation](https://mlsanity.com/paper/2208.12448/) (2022)
- [Decomposed Soft Prompt Guided Fusion Enhancing for Compositional Zero-Shot Learning](https://mlsanity.com/paper/2211.10681/) (2022)
- [DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learning](https://mlsanity.com/paper/2403.08506/) (2024)
- [A Novel Perspective to Zero-shot Learning: Towards an Alignment of Manifold Structures via Semantic Feature Expansion](https://mlsanity.com/paper/2004.14795/) (2020)
- [SRCD: Semantic Reasoning with Compound Domains for Single-Domain Generalized Object Detection](https://mlsanity.com/paper/2307.01750/) (2023)
- [Syntactically Guided Generative Embeddings for Zero-Shot Skeleton Action Recognition](https://mlsanity.com/paper/2101.11530/) (2021)
- [Zero-shot Skeleton-based Action Recognition via Mutual Information Estimation and Maximization](https://mlsanity.com/paper/2308.03950/) (2023)
- [Personalized Federated Learning with Contextualized Generalization](https://mlsanity.com/paper/2106.13044/) (2021)
- [CaDM: Codec-aware Diffusion Modeling for Neural-enhanced Video Streaming](https://mlsanity.com/paper/2211.08428/) (2022)
- [Part-aware Unified Representation of Language and Skeleton for Zero-shot Action Recognition](https://mlsanity.com/paper/2406.13327/) (2024)
- [Towards Unbiased Multi-label Zero-Shot Learning with Pyramid and Semantic Attention](https://mlsanity.com/paper/2203.03483/) (2022)
- [Prompt-Guided Zero-Shot Anomaly Action Recognition using Pretrained Deep Skeleton Features](https://mlsanity.com/paper/2303.15167/) (2023)
- [Personalized Federated Domain-Incremental Learning based on Adaptive Knowledge Matching](https://mlsanity.com/paper/2407.05005/) (2024)
- [Cross-Modal Learning with 3D Deformable Attention for Action Recognition](https://mlsanity.com/paper/2212.05638/) (2022)
- [Unified Multi-modal Unsupervised Representation Learning for Skeleton-based Action Understanding](https://mlsanity.com/paper/2311.03106/) (2023)
- [AMS-SFE: Towards an Alignment of Manifold Structures via Semantic Feature Expansion for Zero-shot Learning](https://mlsanity.com/paper/1904.06254/) (2019)
- [Skeleton based Zero Shot Action Recognition in Joint Pose-Language Semantic Space](https://mlsanity.com/paper/1911.11344/) (2019)
- [Fine-Grained Side Information Guided Dual-Prompts for Zero-Shot Skeleton Action Recognition](https://mlsanity.com/paper/2404.07487/) (2024)
- [SA-DVAE: Improving Zero-Shot Skeleton-Based Action Recognition by Disentangled Variational Autoencoders](https://mlsanity.com/paper/2407.13460/) (2024)

## Built on by (most cited first, 13 of 13)

- [Towards Robust and Realistic Human Pose Estimation via WiFi Signals](https://mlsanity.com/paper/2501.09411/) (2025)
- [Learning by Neighbor-Aware Semantics, Deciding by Open-form Flows: Towards Robust Zero-Shot Skeleton Action Recognition](https://mlsanity.com/paper/2511.09388/) (2025)
- [Universal Skeleton Understanding via Differentiable Rendering and MLLMs](https://mlsanity.com/paper/2603.18003/) (2026)
- [SkeletonContext: Skeleton-side Context Prompt Learning for Zero-Shot Skeleton-based Action Recognition](https://mlsanity.com/paper/2603.29692/) (2026)
- [SkeletonAgent: An Agentic Interaction Framework for Skeleton-based Action Recognition](https://mlsanity.com/paper/2511.22433/) (2025)
- [DynaPURLS: Dynamic Refinement of Part-Aware Representations for Skeleton-Based Zero-Shot Action Recognition](https://mlsanity.com/paper/2512.11941/) (2025)
- [Toward Universal Skeleton-Based Action Recognition across Heterogeneous Skeletons and Open Vocabularies](https://mlsanity.com/paper/2604.17013/) (2026)
- [Marrying Text-to-Motion Generation with Skeleton-Based Action Recognition](https://mlsanity.com/paper/2604.17090/) (2026)
- [Beyond Binary Contrast: Modeling Continuous Skeleton Action Spaces with Transitional Anchors](https://mlsanity.com/paper/2604.17914/) (2026)
- [PoseBridge: Bridging the Skeletonization Gap for Zero-Shot Skeleton-Based Action Recognition](https://mlsanity.com/paper/2605.11497/) (2026)
- [Cross-Domain Human Action Recognition from Multiview Motion and Textual Descriptions](https://mlsanity.com/paper/2605.22697/) (2026)
- [Partial Skeleton Visibility for Action Recognition: A Constrained Field-of-View Approach](https://mlsanity.com/paper/2607.00716/) (2026)
- [Visual Anchoring in Diffusion: Multimodal Zero-Shot Skeleton Action Recognition](https://mlsanity.com/paper/2608.04623/) (2026)

## Abstract (arXiv)

Zero-shot skeleton action recognition is a non-trivial task that requires robust unseen generalization with prior knowledge from only seen classes and shared semantics. Existing methods typically build the skeleton-semantics interactions by uncontrollable mappings and conspicuous representations, thereby can hardly capture the intricate and fine-grained relationship for effective cross-modal transferability. To address these issues, we propose a novel dyNamically Evolving dUal skeleton-semantic syneRgistic framework with the guidance of cOntext-aware side informatioN (dubbed Neuron), to explore more fine-grained cross-modal correspondence from micro to macro perspectives at both spatial and temporal levels, respectively. Concretely, 1) we first construct the spatial-temporal evolving micro-prototypes and integrate dynamic context-aware side information to capture the intricate and synergistic skeleton-semantic correlations step-by-step, progressively refining cross-model alignment; and 2) we introduce the spatial compression and temporal memory mechanisms to guide the growth of spatial-temporal micro-prototypes, enabling them to absorb structure-related spatial representations and regularity-dependent temporal patterns. Notably, such processes are analogous to the learning and growth of neurons, equipping the framework with the capacity to generalize to novel unseen action categories. Extensive experiments on various benchmark datasets demonstrated the superiority of the proposed method.