On Aligning Hierarchical Standardized Embedding for Audio-visual Generalized Zero-shot Learning

• URL: http://arxiv.org/abs/2606.11602v1
• Date: Wed, 10 Jun 2026 02:59:13 GMT
• Title: On Aligning Hierarchical Standardized Embedding for Audio-visual Generalized Zero-shot Learning
• Abstract summary: Audio-visual Generalized Zero-shot Learning (AV-GZSL) is a challenging task that aims to classify both seen and unseen objects or scenes.<n>Recent studies primarily focus on fusing or aligning audio and visual features to generate more informative audio-visual embeddings.<n>We propose a method termed Aligning Hierarchical Standardized Embedding (AHSE), which enables hierarchical alignment of standardized audio-visual and textual embeddings.
• Score: 31.446813732779344
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Audio-visual Generalized Zero-shot Learning (AV-GZSL) is a challenging task that aims to classify both seen and unseen objects or scenes by integrating data from audio and visual modalities. Recent studies primarily focus on fusing or aligning audio and visual features to generate more informative audio-visual embeddings. Also, aligning the audio-visual and textual features of most existing methods relies solely on the optimization objectives. However, those methods neglect the inherent distributional and structural differences between audio-visual and textual modalities. To address this limitation, we propose a method termed Aligning Hierarchical Standardized Embedding (AHSE), which enables hierarchical alignment of standardized audio-visual and textual embeddings within a shared embedding space. Specifically, we first apply Z-score standardization to the fused audio-visual and textual embeddings to reduce distributional mismatches. We then introduce a hierarchical alignment strategy that minimizes discrepancies at the semantic, class, and batch levels, thereby constructing a more robust and well-structured embedding space. This strategy not only preserves semantic and inter-class relationships but also maintains spatial consistency within each batch. Extensive experiments on three benchmark datasets: VGGSound-GZSL, UCF-GZSL, and ActivityNet-GZSL, demonstrate that AHSE achieves competitive performance in zero-shot learning.

Related papers

• Acoustically Grounded Cost Learning for Open-Vocabulary Audio-Visual Semantic Segmentation [95.88527433039519]
  Open-Vocabulary Audio-Visual Semantic (OV-AVSS) aims to perform pixel-level segmentation of sound-emitting objects from an open set of categories.<n>We reformulate the objective to be category-specific and propose a novel Acoustically Grounded Cost Learning framework.<n>Experiments on the AVSBench-OV dataset demonstrate that our method significantly outperforms previous state-of-the-art approaches.
  arXiv  Detail & Related papers  (2026-08-29T08:02:27Z)
• Hierarchical Semantic-Constrained Heterogeneous Graph for Audio-Visual Event Localization [69.67186845167568]
  Open-vocabulary audio-visual event localization (OV-AVEL) jointly models audio-visual cues to recognize and temporally localize events.<n>Existing methods primarily learn joint audio-visual representations in Euclidean space.<n>We propose a hierarchical semantic constrained heterogeneous graph (HSCHG) for audio-visual event localization framework.
  arXiv  Detail & Related papers  (2026-06-05T08:23:58Z)
• Implicit Counterfactual Learning for Audio-Visual Segmentation [50.69377287012591]
  We propose the implicit counterfactual framework (ICF) to achieve unbiased cross-modal understanding.<n>Due to the lack of semantics, heterogeneous representations may lead to erroneous matches.<n>We introduce the multi-granularity implicit text (MIT) involving video-, segment- and frame-level as the bridge to establish the modality-shared space.
  arXiv  Detail & Related papers  (2025-07-28T11:46:35Z)
• CAV-MAE Sync: Improving Contrastive Audio-Visual Mask Autoencoders via Fine-Grained Alignment [76.32508013503653]
  We propose CAV-MAE Sync as a simple yet effective extension of the original CAV-MAE framework for self-supervised audio-visual learning.<n>We tackle the mismatch between modalities by treating audio as a temporal sequence aligned with video frames, rather than using global representations.<n>We improve spatial localization by introducing learnable register tokens that reduce semantic load on patch tokens.
  arXiv  Detail & Related papers  (2025-05-02T12:59:58Z)
• Classifier-Guided Captioning Across Modalities [69.75111271002137]
  We introduce a method to adapt captioning networks to the semantics of alternative settings, such as capturing audibility in audio captioning.<n>Our framework consists of two main components: (i) a frozen captioning system incorporating a language model (LM), and (ii) a text classifier that guides the captioning system.<n> Notably, when combined with an existing zero-shot audio captioning system, our framework improves its quality and sets state-of-the-art performance in zero-shot audio captioning.
  arXiv  Detail & Related papers  (2025-01-03T18:09:26Z)
• Audio-visual Generalized Zero-shot Learning the Easy Way [20.60905505473906]
  We introduce EZ-AVGZL, which aligns audio-visual embeddings with transformed text representations. We conduct extensive experiments on VGGSound-GZSL, UCF-GZSL, and ActivityNet-GZSL benchmarks.
  arXiv  Detail & Related papers  (2024-07-18T01:57:16Z)
• Stepping Stones: A Progressive Training Strategy for Audio-Visual Semantic Segmentation [7.124066540020968]
  Audio-Visual (AVS) aims to achieve pixel-level localization of sound sources in videos, while Audio-Visual Semantic (AVSS) pursues semantic understanding of audio-visual scenes. Previous methods have struggled to handle this mashup of objectives in end-to-end training, resulting in insufficient learning and sub-optimization. We propose a two-stage training strategy called textitStepping Stones, which decomposes the AVSS task into two simple subtasks from localization to semantic understanding, which are fully optimized in each stage to achieve step-by-step global optimization
  arXiv  Detail & Related papers  (2024-07-16T15:08:30Z)
• Label-anticipated Event Disentanglement for Audio-Visual Video Parsing [61.08434062821899]
  We introduce a new decoding paradigm, underlinelabel sunderlineemunderlineantic-based underlineprojection (LEAP) LEAP works by iteratively projecting encoded latent features of audio/visual segments onto semantically independent label embeddings. To facilitate the LEAP paradigm, we propose a semantic-aware optimization strategy, which includes a novel audio-visual semantic similarity loss function.
  arXiv  Detail & Related papers  (2024-07-11T01:57:08Z)
• QDFormer: Towards Robust Audiovisual Segmentation in Complex Environments with Quantization-based Semantic Decomposition [47.103732403296654]
  Multi-source semantic space can be represented as the Cartesian product of single-source sub-spaces. We introduce a global-to-local quantization mechanism, which distills knowledge from stable global (clip-level) features into local (frame-level) ones. Experiments demonstrate that our semantically decomposed audio representation significantly improves AVS performance.
  arXiv  Detail & Related papers  (2023-09-29T20:48:44Z)

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.