Syntactically Guided Generative Embeddings for Zero-Shot Skeleton Action Recognition

• URL: http://arxiv.org/abs/2101.11530v1
• Date: Wed, 27 Jan 2021 16:34:27 GMT
• Title: Syntactically Guided Generative Embeddings for Zero-Shot Skeleton Action Recognition
• Abstract summary: We introduce SynSE, a novel syntactically guided generative approach for Zero-Shot Learning (ZSL) Our end-to-end approach learns progressively refined generative embedding spaces constrained within and across the involved modalities (visual, language) Our results demonstrate SynSE's state of the art performance in both ZSL and Generalized Zero-Shot Learning (GZSL) settings.
• Score: 9.30315673109153
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce SynSE, a novel syntactically guided generative approach for Zero-Shot Learning (ZSL). Our end-to-end approach learns progressively refined generative embedding spaces constrained within and across the involved modalities (visual, language). The inter-modal constraints are defined between action sequence embedding and embeddings of Parts of Speech (PoS) tagged words in the corresponding action description. We deploy SynSE for the task of skeleton-based action sequence recognition. Our design choices enable SynSE to generalize compositionally, i.e., recognize sequences whose action descriptions contain words not encountered during training. We also extend our approach to the more challenging Generalized Zero-Shot Learning (GZSL) problem via a confidence-based gating mechanism. We are the first to present zero-shot skeleton action recognition results on the large-scale NTU-60 and NTU-120 skeleton action datasets with multiple splits. Our results demonstrate SynSE's state of the art performance in both ZSL and GZSL settings compared to strong baselines on the NTU-60 and NTU-120 datasets.

Related papers

• $\mathcal{S}^2$IT: Stepwise Syntax Integration Tuning for Large Language Models in Aspect Sentiment Quad Prediction [51.165432266846096]
  syntactic structure information is underutilized in the generative paradigm of large language models (LLMs)<n>S2IT progressively integrates syntactic structure knowledge into LLMs through a multi-step tuning process.<n> Experiments demonstrate that S2IT significantly improves state-of-the-art performance across multiple datasets.
  arXiv  Detail & Related papers  (2026-04-25T13:24:29Z)
• Novel Semantic Prompting for Zero-Shot Action Recognition [0.0]
  Zero-shot action recognition relies on transferring knowledge from vision-language models to unseen actions using semantic descriptions.<n>We introduce SP-CLIP, a lightweight framework that augments frozen vision-language models with structured semantic prompts describing actions at multiple levels of abstraction.
  arXiv  Detail & Related papers  (2026-03-09T12:07:55Z)
• STELLA: Guiding Large Language Models for Time Series Forecasting with Semantic Abstractions [4.169671705130711]
  We propose STELLA, a framework that mines and injects structured supplementary and complementary information.<n>StELLA employs a dynamic semantic abstraction mechanism that decouples input series into trend, seasonality, and residual components.<n>Experiments on eight benchmark datasets demonstrate that STELLA outperforms state-of-the-art methods in long- and short-term forecasting.
  arXiv  Detail & Related papers  (2025-12-04T14:56:36Z)
• Enhancing Spatio-Temporal Zero-shot Action Recognition with Language-driven Description Attributes [54.50887214639301]
  We propose an innovative approach that harnesses web-crawled descriptions, leveraging a large-language model to extract relevant keywords.<n>This method reduces the need for human annotators and eliminates the laborious manual process of attribute data creation.<n>In our zero-shot experiments, our model achieves accuracies of 81.0%, 53.1%, and 68.9% on UCF-101, HMDB-51, and Kinetics-600, respectively.
  arXiv  Detail & Related papers  (2025-10-31T07:45:44Z)
• Generative Sign-description Prompts with Multi-positive Contrastive Learning for Sign Language Recognition [9.044039469025009]
  We propose a novel Generative Sign-description Prompts Multi-positive Contrastive learning (GSP-MC) method to produce precise multipart descriptions.<n>The GSP-MC method also employs a dual-encoder architecture to bidirectionally align hierarchical skeleton features with multiple text descriptions.<n> Experiments demonstrate state-of-the-art performance against existing methods on the Chinese SLR500 (reaching 97.1%) and Turkish AUTSL datasets (97.07% accuracy)
  arXiv  Detail & Related papers  (2025-05-05T00:57:57Z)
• Refining Sentence Embedding Model through Ranking Sentences Generation with Large Language Models [60.00178316095646]
  Sentence embedding is essential for many NLP tasks, with contrastive learning methods achieving strong performance using datasets like NLI. Recent studies leverage large language models (LLMs) to generate sentence pairs, reducing annotation dependency. We propose a method for controlling the generation direction of LLMs in the latent space. Unlike unconstrained generation, the controlled approach ensures meaningful semantic divergence. Experiments on multiple benchmarks demonstrate that our method achieves new SOTA performance with a modest cost in ranking sentence synthesis.
  arXiv  Detail & Related papers  (2025-02-19T12:07:53Z)
• Align-SLM: Textless Spoken Language Models with Reinforcement Learning from AI Feedback [50.84142264245052]
  This work introduces the Align-SLM framework to enhance the semantic understanding of textless Spoken Language Models (SLMs) Our approach generates multiple speech continuations from a given prompt and uses semantic metrics to create preference data for Direct Preference Optimization (DPO) We evaluate the framework using ZeroSpeech 2021 benchmarks for lexical and syntactic modeling, the spoken version of the StoryCloze dataset for semantic coherence, and other speech generation metrics, including the GPT4-o score and human evaluation.
  arXiv  Detail & Related papers  (2024-11-04T06:07:53Z)
• Part-aware Unified Representation of Language and Skeleton for Zero-shot Action Recognition [57.97930719585095]
  We introduce Part-aware Unified Representation between Language and Skeleton (PURLS) to explore visual-semantic alignment at both local and global scales. Our approach is evaluated on various skeleton/language backbones and three large-scale datasets. The results showcase the universality and superior performance of PURLS, surpassing prior skeleton-based solutions and standard baselines from other domains.
  arXiv  Detail & Related papers  (2024-06-19T08:22:32Z)
• An Information Compensation Framework for Zero-Shot Skeleton-based Action Recognition [49.45660055499103]
  Zero-shot human skeleton-based action recognition aims to construct a model that can recognize actions outside the categories seen during training. Previous research has focused on aligning sequences' visual and semantic spatial distributions. We introduce a new loss function sampling method to obtain a tight and robust representation.
  arXiv  Detail & Related papers  (2024-06-02T06:53:01Z)
• Fine-Grained Side Information Guided Dual-Prompts for Zero-Shot Skeleton Action Recognition [18.012159340628557]
  We propose a novel method via Side information and dual-prompts learning for skeleton-based zero-shot action recognition (STAR) at the fine-grained level. Our method achieves state-of-the-art performance in ZSL and GZSL settings on datasets.
  arXiv  Detail & Related papers  (2024-04-11T05:51:06Z)
• LAC: Latent Action Composition for Skeleton-based Action Segmentation [21.797658771678066]
  Skeleton-based action segmentation requires recognizing composable actions in untrimmed videos. Current approaches decouple this problem by first extracting local visual features from skeleton sequences and then processing them by a temporal model to classify frame-wise actions. We propose Latent Action Composition (LAC), a novel self-supervised framework aiming at learning from synthesized composable motions for skeleton-based action segmentation.
  arXiv  Detail & Related papers  (2023-08-28T11:20:48Z)
• Leaping Into Memories: Space-Time Deep Feature Synthesis [93.10032043225362]
  We propose LEAPS, an architecture-independent method for synthesizing videos from internal models. We quantitatively and qualitatively evaluate the applicability of LEAPS by inverting a range of architectures convolutional attention-based on Kinetics-400.
  arXiv  Detail & Related papers  (2023-03-17T12:55:22Z)
• ProCC: Progressive Cross-primitive Compatibility for Open-World Compositional Zero-Shot Learning [29.591615811894265]
  Open-World Compositional Zero-shot Learning (OW-CZSL) aims to recognize novel compositions of state and object primitives in images with no priors on the compositional space. We propose a novel method, termed Progressive Cross-primitive Compatibility (ProCC), to mimic the human learning process for OW-CZSL tasks.
  arXiv  Detail & Related papers  (2022-11-19T10:09:46Z)
• Token-level Sequence Labeling for Spoken Language Understanding using Compositional End-to-End Models [94.30953696090758]
  We build compositional end-to-end spoken language understanding systems. By relying on intermediate decoders trained for ASR, our end-to-end systems transform the input modality from speech to token-level representations. Our models outperform both cascaded and direct end-to-end models on a labeling task of named entity recognition.
  arXiv  Detail & Related papers  (2022-10-27T19:33:18Z)
• Inducing Transformer's Compositional Generalization Ability via Auxiliary Sequence Prediction Tasks [86.10875837475783]
  Systematic compositionality is an essential mechanism in human language, allowing the recombination of known parts to create novel expressions. Existing neural models have been shown to lack this basic ability in learning symbolic structures. We propose two auxiliary sequence prediction tasks that track the progress of function and argument semantics.
  arXiv  Detail & Related papers  (2021-09-30T16:41:19Z)
• Pose-Guided Sign Language Video GAN with Dynamic Lambda [0.41998444721319206]
  We propose a novel approach for the of sign language videos using GANs. We use the human semantic synthesis of the Soft-Gated Warping-GAN from to produce photorealistic videos guided by region-level spatial layouts.
  arXiv  Detail & Related papers  (2021-05-06T15:12:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.