MD2PLL: a meta-learning framework for robust disambiguation in partial-label visual recognition

Mutual Information Guided Reinforcement Learning for Ambiguous Label Disambiguation

Confidence-Aware Relation Contrastive Learning for Partial Multi-Label Learning

Partial multi-label learning (PML) learns from over-complete candidate labels: All true labels are included in the candidate set, but irrelevant labels may also be annotated. Existing PML methods mainly reduce this ambiguity during classifier training by estimating label reliability, recovering latent label distributions, or modeling label correlations. This paper studies a complementary question: how can PML supervision be used for contrastive representation pretraining? The main difficulty is relation construction. Raw candidate label overlap is not a reliable contrastive signal, because two images can share only false candidate labels. We therefore formulate PML pretraining as a relation disambiguation problem and propose a confidence-aware relation contrastive learning framework. The method estimates candidate label confidence, uses it to construct semantic relation distributions over current and queued samples, and aligns the embedding distribution with these confidence-weighted relations. The same candidate label confidence semantics are used during downstream fine-tuning, keeping non-candidate labels as reliable negatives while softly disambiguating the candidate set. Across VOC2007, NUS-WIDE, and COCO2014, the method improves subset accuracy and F1-oriented measures under moderate and severe partial-label noise. For example, on VOC2007 at p=0.8, it improves subset accuracy from 0.3322 to 0.5269 and micro-F1 from 0.6710 to 0.7617 over PML-CD. However, the CUB200 results further identify a limitation: fine-grained attribute prediction is less consistently served by the present global representation pretraining design.

PseudoMapLabeler: Confidence-Aware Pseudo-Label Generation for Semi-Supervised Online Mapping

This work proposes a teacher-student semi-supervised learning framework that generates high-quality pseudo-labels from unlabeled data through confidence-aware map refinement, and introduces a spatial clipping technique that selectively preserves high-confidence regions while removing unreliable segments.

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation

By separating different pseudo-label reliability regions into specialized adaptation paths, TriNoL improves robustness to noisy supervision while keeping the training cost low.

G2D: Generative-to-Discriminative Collaborative Inference for Zero-Shot Image Classification

G2D is proposed, a training-free framework that uses a generative VLM to verify CLIP-retrieved candidates against the image and transfers to DCLIP, WaffleCLIP, and CuPL, supporting a practical interface between discriminative proposal and generative visual reasoning.

PaSta: Noisy Node Classification with Partial Label Learning

This paper proposes a novel Partial label-based Self-training framework (PaSta) that leverages partial label learning technique to overcome the limitations of existing methods and designs a partial label-based classification model with two well-crafted loss functions to guide the model learning at both label and representation spaces.