Multi-label zero-shot learning

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

3 leaderboard tables shown for this task, 3 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

2 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

15 shown of 15 papers with code (27 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Label-Embedding for Image Classification
  30 Mar 2015 2 repositories listed
  Attributes act as intermediate representations that enable parameter sharing between classes, a must when training data is scarce.
• Zero-Shot Learning by Convex Combination of Semantic Embeddings
  19 Dec 2013 2 repositories listed Syntology ran 0 of 3 samples · 3 unverified · 3 pointer-only (licence)
  In other cases the semantic embedding space is established by an independent natural language processing task, and then the image transformation into that space is learned in a second stage.
• CLIP-Decoder : ZeroShot Multilabel Classification using Multimodal CLIP Aligned Representation
  21 Jun 2024 1 repository listed Syntology ran 2 of 3 samples · 1 unverified · 3 pointer-only (licence)
  Our method achieves an absolute increase of 3.
• Pseudo-Prompt Generating in Pre-trained Vision-Language Models for Multi-Label Medical Image Classification
  10 May 2024 1 repository listed
  The task of medical image recognition is notably complicated by the presence of varied and multiple pathological indications, presenting a unique challenge in multi-label classification with unseen labels.
• Label Propagation for Zero-shot Classification with Vision-Language Models
  5 Apr 2024 1 repository listed Syntology ran 1 of 2 samples · 1 unverified
  We leverage the graph structure of the unlabeled data and introduce ZLaP, a method based on label propagation (LP) that utilizes geodesic distances for classification.
• Open-Vocabulary Multi-Label Classification via Multi-Modal Knowledge Transfer
  5 Jul 2022 1 repository listed Syntology ran 3 of 7 samples · 4 unverified
  Specifically, our method exploits multi-modal knowledge of image-text pairs based on a vision and language pre-training (VLP) model.
• ML-Decoder: Scalable and Versatile Classification Head
  25 Nov 2021 1 repository listed Syntology ran 2 of 5 samples · 3 unverified
  In this paper, we introduce ML-Decoder, a new attention-based classification head.
• Discriminative Region-based Multi-Label Zero-Shot Learning
  20 Aug 2021 1 repository listed Syntology ran 7 of 7 samples · 0 unverified
  We note that the best existing multi-label ZSL method takes a shared approach towards attending to region features with a common set of attention maps for all the classes.
• Contrastive Language-Image Pre-training for the Italian Language
  19 Aug 2021 1 repository listed
  CLIP (Contrastive Language-Image Pre-training) is a very recent multi-modal model that jointly learns representations of images and texts.
• Semantic Diversity Learning for Zero-Shot Multi-label Classification
  12 May 2021 1 repository listed
  We argue that using a single embedding vector to represent an image, as commonly practiced, is not sufficient to rank both relevant seen and unseen labels accurately.
• Generative Multi-Label Zero-Shot Learning
  27 Jan 2021 1 repository listed
  Nevertheless, computing reliable attention maps for unseen classes during inference in a multi-label setting is still a challenge.
• Interaction Compass: Multi-Label Zero-Shot Learning of Human-Object Interactions via Spatial Relations
  1 Jan 2021 1 repository listed
  We study the problem of multi-label zero-shot recognition in which labels are in the form of human-object interactions (combinations of actions on objects), each image may contain multiple interactions and some…
• A Shared Multi-Attention Framework for Multi-Label Zero-Shot Learning
  1 Jun 2020 1 repository listed
  Therefore, instead of generating attentions for unseen labels which have unknown behaviors and could focus on irrelevant regions due to the lack of any training sample, we let the unseen labels select among a set of…
• Zero-shot Learning for Audio-based Music Classification and Tagging
  5 Jul 2019 1 repository listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  Audio-based music classification and tagging is typically based on categorical supervised learning with a fixed set of labels.
• Multi-Label Zero-Shot Learning with Structured Knowledge Graphs
  17 Nov 2017 1 repository listed
  In this paper, we propose a novel deep learning architecture for multi-label zero-shot learning (ML-ZSL), which is able to predict multiple unseen class labels for each input instance.

Syntology lines on 7 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.