# CLIP-Decoder : ZeroShot Multilabel Classification using Multimodal CLIP Aligned Representation

arXiv: https://arxiv.org/abs/2406.14830 · HTML: https://mlsanity.com/paper/2406.14830/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 23 references that are in the map.
- First posted on arXiv 21 Jun 2024.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [ImageNet-21K Pretraining for the Masses](https://mlsanity.com/paper/2104.10972/) (2021)
- [Asymmetric Loss For Multi-Label Classification](https://mlsanity.com/paper/2009.14119/) (2020)
- [Multi-Label Image Recognition with Graph Convolutional Networks](https://mlsanity.com/paper/1904.03582/) (2019)
- [Query2Label: A Simple Transformer Way to Multi-Label Classification](https://mlsanity.com/paper/2107.10834/) (2021)
- [Fast Transformers with Clustered Attention](https://mlsanity.com/paper/2007.04825/) (2020)
- [Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets](https://mlsanity.com/paper/2007.09654/) (2020)
- [TResNet: High Performance GPU-Dedicated Architecture](https://mlsanity.com/paper/2003.13630/) (2020)
- [ML-Decoder: Scalable and Versatile Classification Head](https://mlsanity.com/paper/2111.12933/) (2021)
- [Residual Attention: A Simple but Effective Method for Multi-Label Recognition](https://mlsanity.com/paper/2108.02456/) (2021)
- [Learning to Discover Multi-Class Attentional Regions for Multi-Label Image Recognition](https://mlsanity.com/paper/2007.01755/) (2020)
- [Exploit Bounding Box Annotations for Multi-label Object Recognition](https://mlsanity.com/paper/1504.05843/) (2015)
- [Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection](https://mlsanity.com/paper/1809.05884/) (2018)
- [Multi-label Classification with Partial Annotations using Class-aware Selective Loss](https://mlsanity.com/paper/2110.10955/) (2021)
- [Semantic Diversity Learning for Zero-Shot Multi-label Classification](https://mlsanity.com/paper/2105.05926/) (2021)
- [Contextual Word Representations: A Contextual Introduction](https://mlsanity.com/paper/1902.06006/) (2019)
- [Speech-text based multi-modal training with bidirectional attention for improved speech recognition](https://mlsanity.com/paper/2211.00325/) (2022)

## Built on by (most cited first, 9 of 9)

- [CausalCLIPSeg: Unlocking CLIP's Potential in Referring Medical Image Segmentation with Causal Intervention](https://mlsanity.com/paper/2503.15949/) (2025)
- [Organizing Unstructured Image Collections using Natural Language](https://mlsanity.com/paper/2410.05217/) (2024)
- [Captured by Captions: On Memorization and its Mitigation in CLIP Models](https://mlsanity.com/paper/2502.07830/) (2025)
- [SoK: A Systems Perspective on Compound AI Threats and Countermeasures](https://mlsanity.com/paper/2411.13459/) (2024)
- [Multi-Modality Driven LoRA for Adverse Condition Depth Estimation](https://mlsanity.com/paper/2412.20162/) (2024)
- [Vision Transformers: the threat of realistic adversarial patches](https://mlsanity.com/paper/2509.21084/) (2025)
- [OTTER: Open-Tagging via Text-Image Representation for Multi-modal Understanding](https://mlsanity.com/paper/2510.00652/) (2025)
- [User to Video: A Model for Spammer Detection Inspired by Video Classification Technology](https://mlsanity.com/paper/2510.06233/) (2025)
- [SMETA-ZSL:Semantic Meta-Alignment for Zero-Shot Threat Classification](https://mlsanity.com/paper/2607.09936/) (2026)

## Abstract (arXiv)

Multi-label classification is an essential task utilized in a wide variety of real-world applications. Multi-label zero-shot learning is a method for classifying images into multiple unseen categories for which no training data is available, while in general zero-shot situations, the test set may include observed classes. The CLIP-Decoder is a novel method based on the state-of-the-art ML-Decoder attention-based head. We introduce multi-modal representation learning in CLIP-Decoder, utilizing the text encoder to extract text features and the image encoder for image feature extraction. Furthermore, we minimize semantic mismatch by aligning image and word embeddings in the same dimension and comparing their respective representations using a combined loss, which comprises classification loss and CLIP loss. This strategy outperforms other methods and we achieve cutting-edge results on zero-shot multilabel classification tasks using CLIP-Decoder. Our method achieves an absolute increase of 3.9% in performance compared to existing methods for zero-shot learning multi-label classification tasks. Additionally, in the generalized zero-shot learning multi-label classification task, our method shows an impressive increase of almost 2.3%.