# A Training-Free Framework for Open-Vocabulary Image Segmentation and Recognition with EfficientNet and CLIP

arXiv: https://arxiv.org/abs/2510.19333 · HTML: https://mlsanity.com/paper/2510.19333/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Prompt learning and adaptation of CLIP, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 26 of its 33 references that are in the map.
- First posted on arXiv 22 Oct 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Prompt learning and adaptation of CLIP](https://mlsanity.com/mllm/prompt-learning-and-adaptation-of-clip/)

## Builds on (most cited first, 26 of 26)

- [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)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://mlsanity.com/paper/2107.06278/) (2021)
- [Open-vocabulary Object Detection via Vision and Language Knowledge Distillation](https://mlsanity.com/paper/2104.13921/) (2021)
- [Extract Free Dense Labels from CLIP](https://mlsanity.com/paper/2112.01071/) (2021)
- [Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP](https://mlsanity.com/paper/2210.04150/) (2022)
- [Conditional Convolutions for Instance Segmentation](https://mlsanity.com/paper/2003.05664/) (2020)
- [Unsupervised Semantic Segmentation by Distilling Feature Correspondences](https://mlsanity.com/paper/2203.08414/) (2022)
- [Decoupling Zero-Shot Semantic Segmentation](https://mlsanity.com/paper/2112.07910/) (2021)
- [Cut and Learn for Unsupervised Object Detection and Instance Segmentation](https://mlsanity.com/paper/2301.11320/) (2023)
- [Towards Open Vocabulary Learning: A Survey](https://mlsanity.com/paper/2306.15880/) (2023)
- [Open-Vocabulary Universal Image Segmentation with MaskCLIP](https://mlsanity.com/paper/2208.08984/) (2022)
- [Diffuse, Attend, and Segment: Unsupervised Zero-Shot Segmentation using Stable Diffusion](https://mlsanity.com/paper/2308.12469/) (2023)
- [ReCo: Retrieve and Co-segment for Zero-shot Transfer](https://mlsanity.com/paper/2206.07045/) (2022)
- [Perceptual Grouping in Contrastive Vision-Language Models](https://mlsanity.com/paper/2210.09996/) (2022)
- [Open-World Entity Segmentation](https://mlsanity.com/paper/2107.14228/) (2021)
- [Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations](https://mlsanity.com/paper/2207.05027/) (2022)
- [Unsupervised Universal Image Segmentation](https://mlsanity.com/paper/2312.17243/) (2023)
- [DiffCut: Catalyzing Zero-Shot Semantic Segmentation with Diffusion Features and Recursive Normalized Cut](https://mlsanity.com/paper/2406.02842/) (2024)
- [MarvelOVD: Marrying Object Recognition and Vision-Language Models for Robust Open-Vocabulary Object Detection](https://mlsanity.com/paper/2407.21465/) (2024)
- [A Hierarchical Semantic Distillation Framework for Open-Vocabulary Object Detection](https://mlsanity.com/paper/2503.10152/) (2025)

## Abstract (arXiv)

This paper presents a novel training-free framework for open-vocabulary image segmentation and object recognition (OVSR), which leverages EfficientNetB0, a convolutional neural network, for unsupervised segmentation and CLIP, a vision-language model, for open-vocabulary object recognition. The proposed framework adopts a two stage pipeline: unsupervised image segmentation followed by segment-level recognition via vision-language alignment. In the first stage, pixel-wise features extracted from EfficientNetB0 are decomposed using singular value decomposition to obtain latent representations, which are then clustered using hierarchical clustering to segment semantically meaningful regions. The number of clusters is adaptively determined by the distribution of singular values. In the second stage, the segmented regions are localized and encoded into image embeddings using the Vision Transformer backbone of CLIP. Text embeddings are precomputed using CLIP's text encoder from category-specific prompts, including a generic something else prompt to support open set recognition. The image and text embeddings are concatenated and projected into a shared latent feature space via SVD to enhance cross-modal alignment. Recognition is performed by computing the softmax over the similarities between the projected image and text embeddings. The proposed method is evaluated on standard benchmarks, including COCO, ADE20K, and PASCAL VOC, achieving state-of-the-art performance in terms of Hungarian mIoU, precision, recall, and F1-score. These results demonstrate the effectiveness, flexibility, and generalizability of the proposed framework.