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Talking to DINO: Bridging Self-Supervised Vision Backbones with Language for Open-Vocabulary Segmentation (ICCV 2025) Luca Barsellotti   Lorenzo Bianchi   Nicola Messina   Fabio Carrara   Marcella Cornia   Lorenzo Baraldi   Fabrizio Falchi   Rita Cucchiara About 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. Mapping CLIP Text Embeddings to DINOv2 space with Talk2DINO We can use Talk2DINO to map CLIP text embeddings into the DINOv2 patch embedding space. Demo In `demo.ipynb` we provide a simple example on how to use Talk2DINO for inference on a given image with custom textual categories. Result: > For the full MMCV interface to perform evaluation on segmentation benchmarks, please refer to the original Talk2DINO repository. | Image | Ground Truth | FreeDA | ProxyCLIP | CLIP-DINOiser | Ours (Talk2DINO) | |-----------|------------------|------------|---------------|-------------------|------------------| | | | | | | | | | | | | | | | | | | | | | | | | | | | | Reference If you found this code useful, please cite the following paper:

Talking to DINO: Bridging Self-Supervised Vision Backbones with Language for Open-Vocabulary Segmentation (ICCV 2025) Luca Barsellotti   Lorenzo Bianchi   Nicola Messina   Fabio Carrara   Marcella Cornia   Lorenzo Baraldi   Fabrizio Falchi   Rita Cucchiara About 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. Mapping CLIP Text Embeddings to DINOv2 space with Talk2DINO We can use Talk2DINO to map CLIP text embeddings into the DINOv2 patch embedding space. Demo In `demo.ipynb` we provide a simple example on how to use Talk2DINO for inference on a given image with custom textual categories. Result: > For the full MMCV interface to perform evaluation on segmentation benchmarks, please refer to the original Talk2DINO repository. | Image | Ground Truth | FreeDA | ProxyCLIP | CLIP-DINOiser | Ours (Talk2DINO) | |-----------|------------------|------------|---------------|-------------------|------------------| | | | | | | | | | | | | | | | | | | | | | | | | | | | | Reference If you found this code useful, please cite the following paper:

Talking to DINO: Bridging Self-Supervised Vision Backbones with Language for Open-Vocabulary Segmentation (ICCV 2025) Luca Barsellotti   Lorenzo Bianchi   Nicola Messina   Fabio Carrara   Marcella Cornia   Lorenzo Baraldi   Fabrizio Falchi   Rita Cucchiara About 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. Mapping CLIP Text Embeddings to DINOv3 space with Talk2DINO We can use Talk2DINO to map CLIP text embeddings into the DINOv2 patch embedding space. Demo In `demo.ipynb` we provide a simple example on how to use Talk2DINO for inference on a given image with custom textual categories. Result: > For the full MMCV interface to perform evaluation on segmentation benchmarks, please refer to the original Talk2DINO repository. | Image | Ground Truth | FreeDA | ProxyCLIP | CLIP-DINOiser | Ours (Talk2DINO) | |-----------|------------------|------------|---------------|-------------------|------------------| | | | | | | | | | | | | | | | | | | | | | | | | | | | | Reference If you found this code useful, please cite the following paper: