Adaptive Loss Balancing for Multi-Task Bioacoustic Classification of Bird Species and Call Types

• URL: http://arxiv.org/abs/2607.03304v1
• Date: Fri, 03 Jul 2026 13:16:30 GMT
• Title: Adaptive Loss Balancing for Multi-Task Bioacoustic Classification of Bird Species and Call Types
• Abstract summary: We extend BirdCallNet for joint species and call-type classification on the long-tailed WiWa dataset.<n>We evaluate four bird-domain encoders, with separate species and call-type heads under linear probing, attentive probing, and full fine-tuning.
• Score: 1.03520324742358
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reliable analysis of bird vocalisations in passive acoustic monitoring requires models handling multiple, imbalanced annotation targets. We extend BirdCallNet for joint species and call-type classification on the long-tailed WiWa dataset and investigate how task-loss balancing interacts with pretrained representations and adaptation depth. We evaluate four bird-domain encoders, ConvNeXtBS, EAT, BirdMAE, and ProtoCLR, with separate species and call-type heads under linear probing, attentive probing, and full fine-tuning. A manually tuned fixed objective is compared with homoscedastic uncertainty weighting and Dynamic Weight Averaging across all three adaptation regimes, while GradNorm is evaluated only under full fine-tuning. Results indicate that the factorised multi-task formulation yields the most consistent improvements over the combined single-task baseline for call-type recognition, while its effect on species recognition depends on the adaptation regime. Full fine-tuning is not consistently optimal: ConvNeXtBS achieves the highest mean species performance under linear probing, whereas BirdMAE provides the strongest call-type performance under attentive probing. Adaptive weighting benefits species recognition more consistently than call-type recognition. Uncertainty weighting is particularly effective for species recognition under attentive probing, whereas Dynamic Weight Averaging is generally stronger for the same task under full fine-tuning. GradNorm achieves competitive call-type performance for selected backbones but consistently underperforms other weighting strategies for species recognition and incurs higher computational and memory costs. Overall, the preferred loss-balancing strategy depends on the backbone, adaptation regime, and target task, while frozen-backbone adaptation can provide a more favourable performance-efficiency trade-off than end-to-end fine-tuning.

Related papers

• Tail-Aware Adaptive-k: Query-Adaptive Context Selection for Retrieval-Augmented Generation [0.9786690381850356]
  We propose Tail-Aware Adaptive-k(TAA-k), a training-free framework that operationalizes Extreme Value Theory (EVT) through a localized validation strategy.<n>Under mild monotone likelihood ratio assumptions, TAA-k yields a stable, query-adaptive cutoff corresponding to the earliest noise-dominated position.<n>Experiments on WebQuestions, 2WikiMultiHopQA, and MuSiQue demonstrate that TAA-k achieves near-oracle retrieval quality (F1 within 2-3% of oracle) with orders-of-magnitude efficiency gains over global EVT methods.
  arXiv  Detail & Related papers  (2026-06-10T10:37:19Z)
• Toward Optimal Sampling Rate Selection and Unbiased Classification for Precise Animal Activity Recognition [10.741458442311362]
  Individual-Behavior-Aware Network (IBA-Net) is a novel Individual-Behavior-Aware Network (IBA-Net)<n>IBA-Net fuses data from multiple sampling rates, capturing customized features tailored to various animal behaviors.<n>We conduct experiments on three public datasets covering goat, cattle, and horse activity recognition.
  arXiv  Detail & Related papers  (2026-04-01T06:02:58Z)
• BROTHER: Behavioral Recognition Optimized Through Heterogeneous Ensemble Regularization for Ambivalence and Hesitancy [39.407739937584104]
  Ambivalence and Hesitancy (A/H) in naturalistic video settings remains a significant challenge in affective computing.<n>We propose a highly regularized, multimodal fusion pipeline to predict A/H at the video level.
  arXiv  Detail & Related papers  (2026-03-15T12:53:41Z)
• K-Sort Eval: Efficient Preference Evaluation for Visual Generation via Corrected VLM-as-a-Judge [51.93484138861584]
  The rapid development of visual generative models raises the need for more scalable and human-aligned evaluation methods.<n>We propose K-Sort Eval, a reliable and efficient VLM-based evaluation framework that integrates posterior correction and dynamic matching.<n>Experiments show that K-Sort Eval delivers evaluation results consistent with K-Sort Arena, typically requiring fewer than 90 model runs.
  arXiv  Detail & Related papers  (2026-02-10T05:07:46Z)
• Anchoring Values in Temporal and Group Dimensions for Flow Matching Model Alignment [61.80228667422234]
  VGPO redefines value estimation across both temporal and group dimensions.<n>It transforms the sparse terminal reward into dense, process-aware value estimates.<n>It replaces standard group normalization with a novel process enhanced by absolute values to maintain a stable optimization signal.
  arXiv  Detail & Related papers  (2025-12-13T16:31:26Z)
• Continual Action Quality Assessment via Adaptive Manifold-Aligned Graph Regularization [53.82400605816587]
  Action Quality Assessment (AQA) quantifies human actions in videos, supporting applications in sports scoring, rehabilitation, and skill evaluation.<n>A major challenge lies in the non-stationary nature of quality distributions in real-world scenarios.<n>We introduce Continual AQA (CAQA), which equips with Continual Learning capabilities to handle evolving distributions.
  arXiv  Detail & Related papers  (2025-10-08T10:09:47Z)
• Can Masked Autoencoders Also Listen to Birds? [2.430300340530418]
  Masked Autoencoders (MAEs) learn rich semantic representations in audio classification through an efficient self-supervised reconstruction task.<n>General-purpose models fail to generalize well when applied directly to fine-grained audio domains.<n>We show that bridging this domain gap requires full-pipeline adaptation, not just domain-specific pretraining data.
  arXiv  Detail & Related papers  (2025-04-17T12:13:25Z)
• On the Role of Feedback in Test-Time Scaling of Agentic AI Workflows [71.92083784393418]
  Agentic AI (systems that autonomously plan and act) are becoming widespread, yet their task success rate on complex tasks remains low.<n>Inference-time alignment relies on three components: sampling, evaluation, and feedback.<n>We introduce Iterative Agent Decoding (IAD), a procedure that repeatedly inserts feedback extracted from different forms of critiques.
  arXiv  Detail & Related papers  (2025-04-02T17:40:47Z)
• SeWA: Selective Weight Average via Probabilistic Masking [51.015724517293236]
  We show that only a few points are needed to achieve better and faster convergence.<n>We transform the discrete selection problem into a continuous subset optimization framework.<n>We derive the SeWA's stability bounds, which are sharper than that under both convex image checkpoints.
  arXiv  Detail & Related papers  (2025-02-14T12:35:21Z)
• Advanced Framework for Animal Sound Classification With Features Optimization [35.2832738406242]
  We propose an automated classification framework applicable to general animal sound classification. Our approach consistently outperforms baseline methods by over 25% in precision, recall, and accuracy.
  arXiv  Detail & Related papers  (2024-07-03T18:33:47Z)
• Balanced Classification: A Unified Framework for Long-Tailed Object Detection [74.94216414011326]
  Conventional detectors suffer from performance degradation when dealing with long-tailed data due to a classification bias towards the majority head categories. We introduce a unified framework called BAlanced CLassification (BACL), which enables adaptive rectification of inequalities caused by disparities in category distribution. BACL consistently achieves performance improvements across various datasets with different backbones and architectures.
  arXiv  Detail & Related papers  (2023-08-04T09:11:07Z)
• Uncertainty Guided Adaptive Warping for Robust and Efficient Stereo Matching [77.133400999703]
  Correlation based stereo matching has achieved outstanding performance. Current methods with a fixed model do not work uniformly well across various datasets. This paper proposes a new perspective to dynamically calculate correlation for robust stereo matching.
  arXiv  Detail & Related papers  (2023-07-26T09:47:37Z)

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.