# Talking to DINO: Bridging Self-Supervised Vision Backbones with Language for Open-Vocabulary Segmentation

arXiv: https://arxiv.org/abs/2411.19331 · HTML: https://mlsanity.com/paper/2411.19331/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- Builds on 55 of its 63 references that are in the map.
- First posted on arXiv 28 Nov 2024.

## Builds on (most cited first, 30 of 55)

- [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)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [Grounded Language-Image Pre-training](https://mlsanity.com/paper/2112.03857/) (2021)
- [Semantic Understanding of Scenes through the ADE20K Dataset](https://mlsanity.com/paper/1608.05442/) (2016)
- [Vision Transformers Need Registers](https://mlsanity.com/paper/2309.16588/) (2023)
- [COCO-Stuff: Thing and Stuff Classes in Context](https://mlsanity.com/paper/1612.03716/) (2016)
- [Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs](https://mlsanity.com/paper/2401.06209/) (2024)
- [Language-driven Semantic Segmentation](https://mlsanity.com/paper/2201.03546/) (2022)
- [EVA: Exploring the Limits of Masked Visual Representation Learning at Scale](https://mlsanity.com/paper/2211.07636/) (2022)
- [RegionCLIP: Region-based Language-Image Pretraining](https://mlsanity.com/paper/2112.09106/) (2021)
- [Detecting Twenty-thousand Classes using Image-level Supervision](https://mlsanity.com/paper/2201.02605/) (2022)
- [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)
- [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://mlsanity.com/paper/2202.11094/) (2022)
- [Scaling Open-Vocabulary Image Segmentation with Image-Level Labels](https://mlsanity.com/paper/2112.12143/) (2021)
- [Scaling Open-Vocabulary Object Detection](https://mlsanity.com/paper/2306.09683/) (2023)
- [Side Adapter Network for Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2302.12242/) (2023)
- [Generalized Decoding for Pixel, Image, and Language](https://mlsanity.com/paper/2212.11270/) (2022)
- [Zero-Shot Semantic Segmentation](https://mlsanity.com/paper/1906.00817/) (2019)
- [Localizing Objects with Self-Supervised Transformers and no Labels](https://mlsanity.com/paper/2109.14279/) (2021)
- [A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language Model](https://mlsanity.com/paper/2112.14757/) (2021)
- [CAT-Seg: Cost Aggregation for Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2303.11797/) (2023)
- [Cut and Learn for Unsupervised Object Detection and Instance Segmentation](https://mlsanity.com/paper/2301.11320/) (2023)
- [SCLIP: Rethinking Self-Attention for Dense Vision-Language Inference](https://mlsanity.com/paper/2312.01597/) (2023)
- [Single-Stage Semantic Segmentation from Image Labels](https://mlsanity.com/paper/2005.08104/) (2020)

## Built on by (most cited first, 30 of 44)

- [GaussTR: Foundation Model-Aligned Gaussian Transformer for Self-Supervised 3D Spatial Understanding](https://mlsanity.com/paper/2412.13193/) (2024)
- [CountingDINO: A Training-free Pipeline for Class-Agnostic Counting using Unsupervised Backbones](https://mlsanity.com/paper/2504.16570/) (2025)
- [RADSeg: Unleashing Parameter and Compute Efficient Zero-Shot Open-Vocabulary Segmentation Using Agglomerative Models](https://mlsanity.com/paper/2511.19704/) (2025)
- [ConceptPose: Training-Free Zero-Shot Object Pose Estimation using Concept Vectors](https://mlsanity.com/paper/2512.09056/) (2025)
- [TRACE: Your Diffusion Model is Secretly an Instance Edge Detector](https://mlsanity.com/paper/2503.07982/) (2025)
- [Taming SAM3 in the Wild: A Concept Bank for Open-Vocabulary Segmentation](https://mlsanity.com/paper/2602.06333/) (2026)
- [VGGT-SLAM++](https://mlsanity.com/paper/2604.06830/) (2026)
- [Resource-Efficient Affordance Grounding with Complementary Depth and Semantic Prompts](https://mlsanity.com/paper/2503.02600/) (2025)
- [ShelfGaussian: Shelf-Supervised Open-Vocabulary Gaussian-based 3D Scene Understanding](https://mlsanity.com/paper/2512.03370/) (2025)
- [PEARL: Geometry Aligns Semantics for Training-Free Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2603.21528/) (2026)
- [Pretext Matters: An Empirical Study of SSL Methods in Medical Imaging](https://mlsanity.com/paper/2603.22649/) (2026)
- [ConInfer: Context-Aware Inference for Training-Free Open-Vocabulary Remote Sensing Segmentation](https://mlsanity.com/paper/2603.29271/) (2026)
- [LEG-SLAM: Real-Time Language-Enhanced Gaussian Splatting for SLAM](https://mlsanity.com/paper/2506.03073/) (2025)
- [Easy3D-Labels: Supervising Semantic Occupancy Estimation with 3D Pseudo-Labels for Automotive Perception](https://mlsanity.com/paper/2509.26087/) (2025)
- [One Patch to Caption Them All: A Unified Zero-Shot Captioning Framework](https://mlsanity.com/paper/2510.02898/) (2025)
- [Toward Stable Semi-Supervised Remote Sensing Segmentation via Co-Guidance and Co-Fusion](https://mlsanity.com/paper/2512.23035/) (2025)
- [F2IDiff: Real-world Image Super-resolution using Feature to Image Diffusion Foundation Model](https://mlsanity.com/paper/2512.24473/) (2025)
- [DiSa: Saliency-Aware Foreground-Background Disentangled Framework for Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2601.20064/) (2026)
- [Enabling Training-Free Text-Based Remote Sensing Segmentation](https://mlsanity.com/paper/2602.17799/) (2026)
- [RS-MTDF: Multi-Teacher Distillation and Fusion for Remote Sensing Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2506.08772/) (2025)
- [FarSLIP: Discovering Effective CLIP Adaptation for Fine-Grained Remote Sensing Understanding](https://mlsanity.com/paper/2511.14901/) (2025)
- [KM-ViPE: Online Tightly Coupled Vision-Language-Geometry Fusion for Open-Vocabulary Semantic SLAM](https://mlsanity.com/paper/2512.01889/) (2025)
- [ReCap: Lightweight Referential Grounding for Coherent Story Visualization](https://mlsanity.com/paper/2604.18575/) (2026)
- [CoCo-SAM3: Harnessing Concept Conflict in Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2604.19648/) (2026)
- [Rethinking Prototype-based Similarity Learning for Few-Shot Object Detection](https://mlsanity.com/paper/2606.23069/) (2026)
- [DINOde: Continuous Vision-Text Alignment for Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2607.21371/) (2026)
- [VoLN: Vision-Only Long-Horizon Navigation---Paradigm, Benchmark, and Method](https://mlsanity.com/paper/2607.21400/) (2026)
- [Talk2SAM: Text-Guided Semantic Enhancement for Complex-Shaped Object Segmentation](https://mlsanity.com/paper/2506.05396/) (2025)
- [SuperQuadricOcc: Real-Time Self-Supervised Semantic Occupancy Estimation with Superquadric Volume Rendering](https://mlsanity.com/paper/2511.17361/) (2025)
- [Generating metamers of human scene understanding](https://mlsanity.com/paper/2601.11675/) (2026)

## Abstract (arXiv)

Open-Vocabulary Segmentation (OVS) aims at segmenting images from free-form textual concepts without predefined training classes. While existing vision-language models such as CLIP can generate segmentation masks by leveraging coarse spatial information from Vision Transformers, they face challenges in spatial localization due to their global alignment of image and text features. Conversely, self-supervised visual models like DINO excel in fine-grained visual encoding but lack integration with language. To bridge this gap, we present Talk2DINO, a novel hybrid approach that combines the spatial accuracy of DINOv2 with the language understanding of CLIP. Our approach aligns the textual embeddings of CLIP to the patch-level features of DINOv2 through a learned mapping function without the need to fine-tune the underlying backbones. At training time, we exploit the attention maps of DINOv2 to selectively align local visual patches with textual embeddings. We show that the powerful semantic and localization abilities of Talk2DINO can enhance the segmentation process, resulting in more natural and less noisy segmentations, and that our approach can also effectively distinguish foreground objects from the background. Experimental results demonstrate that Talk2DINO achieves state-of-the-art performance across several unsupervised OVS benchmarks. Source code and models are publicly available at: https://lorebianchi98.github.io/Talk2DINO/.