SPACE-CLIP: Spatial Perception via Adaptive CLIP Embeddings for Monocular Depth Estimation

• URL: http://arxiv.org/abs/2601.17657v1
• Date: Sun, 25 Jan 2026 02:32:01 GMT
• Title: SPACE-CLIP: Spatial Perception via Adaptive CLIP Embeddings for Monocular Depth Estimation
• Abstract summary: We present SPACE-CLIP, an architecture that unlocks and interprets latent geometric knowledge directly from a frozen CLIP vision encoder.<n>A semantic pathway interprets high-level features, dynamically conditioned on global context.<n>A structural pathway extracts fine-grained spatial details from early layers.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contrastive Language-Image Pre-training (CLIP) has accomplished extraordinary success for semantic understanding but inherently struggles to perceive geometric structure. Existing methods attempt to bridge this gap by querying CLIP with textual prompts, a process that is often indirect and inefficient. This paper introduces a fundamentally different approach using a dual-pathway decoder. We present SPACE-CLIP, an architecture that unlocks and interprets latent geometric knowledge directly from a frozen CLIP vision encoder, completely bypassing the text encoder and its associated textual prompts. A semantic pathway interprets high-level features, dynamically conditioned on global context using feature-wise linear modulation (FiLM). In addition, a structural pathway extracts fine-grained spatial details from early layers. These complementary streams are hierarchically fused, enabling a robust synthesis of semantic context and precise geometry. Extensive experiments on the KITTI benchmark show that SPACE-CLIP dramatically outperforms previous CLIP-based methods. Our ablation studies validate that the synergistic fusion of our dual pathways is critical to this success. SPACE-CLIP offers a new, efficient, and architecturally elegant blueprint for repurposing large-scale vision models. The proposed method is not just a standalone depth estimator, but a readily integrable spatial perception module for the next generation of embodied AI systems, such as vision-language-action (VLA) models. Our model is available at https://github.com/taewan2002/space-clip

Related papers

• SSC-Priors: Exploring Semantic and Visibility Priors to Boost Lidar Semantic Scene Completion [61.665449818739155]
  This paper investigates easy strategies to boost the performance of existing networks for lidar semantic scene completion (SSC)<n>We use semantic pseudo-labels and sensor visibility information to improve existing approaches.<n>We show that endowing input point clouds with semantic pseudo-labels from off-the-shelf segmenters significantly improves the performance of existing SSC models.
  arXiv  Detail & Related papers  (2026-09-15T16:37:46Z)
• DINOde: Continuous Vision-Text Alignment for Open-Vocabulary Semantic Segmentation [53.011370226020695]
  We propose DINOde, an ODE-based framework that aligns CLIP text embeddings with the DINO visual manifold.<n>Our approach employs two complementary components: (i) Semantic Text Flow (STF), which evolves text embeddings toward the DINO manifold through a continuous ODE trajectory, and (ii) Global Context Flow (GCF), which refines the holistic image representation carried by DINO's CLS token.<n>By modeling alignment as a continuous trajectory, DINOde avoids the entanglement inherent in discrete projections and yields more robust cross-modal alignment.
  arXiv  Detail & Related papers  (2026-07-23T14:37:27Z)
• PCA-Seg: Revisiting Cost Aggregation for Open-Vocabulary Semantic and Part Segmentation [58.1914505657064]
  This paper proposes a simple yet effective parallel cost aggregation (PCA-Seg) paradigm to alleviate the challenge of knowledge interference between class-level semantics and spatial context.<n>Experiments on eight benchmarks show that each parallel block in PCA-Seg adds merely 0.35M parameters while achieving state-of-the-art OSPS performance.
  arXiv  Detail & Related papers  (2026-03-18T09:26:43Z)
• Exploring Open-Vocabulary Object Recognition in Images using CLIP [12.96248884328754]
  This paper proposes a novel Open-Vocabulary Object Recognition (OVOR) framework based on a streamlined two-stage strategy.<n>The framework eliminates the need for complex retraining and labor-intensive annotation.<n> Experiments on COCO, Pascal VOC, and ADE20K demonstrate that training-free, CLIP-based encoding without SVD achieves the highest average AP.
  arXiv  Detail & Related papers  (2026-03-06T06:53:32Z)
• Towards Pixel-Level VLM Perception via Simple Points Prediction [27.271487302305726]
  We present SimpleSeg, a strikingly simple yet highly effective approach to endow Multimodal Large Language Models (MLLMs) with native pixel-level perception.<n>Our method reframes segmentation as a simple sequence generation problem: the model directly predicts sequences of points.<n>We find that the standard MLLM architecture possesses a strong, inherent capacity for low-level perception that can be unlocked without any specialized architecture.
  arXiv  Detail & Related papers  (2026-01-27T05:50:40Z)
• SuperCLIP: CLIP with Simple Classification Supervision [88.86549733903314]
  Contrastive Language-Image Pretraining achieves strong generalization in vision-language tasks by aligning images and texts in a shared embedding space.<n>Recent findings show that CLIP-like models still underutilize fine-grained semantic signals in text.<n>We propose SuperCLIP, a framework that augments contrastive learning with classification-based supervision.
  arXiv  Detail & Related papers  (2025-12-16T15:11:53Z)
• SpatialGeo:Boosting Spatial Reasoning in Multimodal LLMs via Geometry-Semantics Fusion [23.86761713752287]
  Multimodal large language models (MLLMs) have achieved significant progress in image and language tasks.<n>Most MLLMs suffer from limited spatial reasoning ability to interpret and infer spatial arrangements in three-dimensional space.<n>We propose a novel vision encoder based on hierarchical fusion of geometry and semantics features, generating spatial-aware visual embedding.
  arXiv  Detail & Related papers  (2025-11-21T15:24:33Z)
• CCL-LGS: Contrastive Codebook Learning for 3D Language Gaussian Splatting [53.15827818829865]
  Methods that rely on 2D priors are prone to a critical challenge: cross-view semantic inconsistencies.<n>We propose CCL-LGS, a novel framework that enforces view-consistent semantic supervision by integrating multi-view semantic cues.<n>Our framework explicitly resolves semantic conflicts while preserving category discriminability.
  arXiv  Detail & Related papers  (2025-05-26T19:09:33Z)
• Is CLIP ideal? No. Can we fix it? Yes! [30.71718499767702]
  Contrastive Language-Image Pre-Training is a popular method for learning multimodal latent spaces with well-organized semantics.<n>Despite its wide range of applications, CLIP's latent space is known to fail at handling complex visual-textual interactions.<n>We propose Cosine Similarity Maps (DCSMs) as a principled and interpretable scoring method for CLIP-like models.
  arXiv  Detail & Related papers  (2025-03-10T23:42:04Z)
• Frozen CLIP: A Strong Backbone for Weakly Supervised Semantic Segmentation [90.35249276717038]
  We propose WeCLIP, a CLIP-based single-stage pipeline, for weakly supervised semantic segmentation. Specifically, the frozen CLIP model is applied as the backbone for semantic feature extraction. A new decoder is designed to interpret extracted semantic features for final prediction.
  arXiv  Detail & Related papers  (2024-06-17T03:49:47Z)
• Cascade-CLIP: Cascaded Vision-Language Embeddings Alignment for Zero-Shot Semantic Segmentation [72.47110803885235]
  We introduce a novel framework named Cascade-CLIP for zero-shot semantic segmentation. Our framework achieves superior zero-shot performance on segmentation benchmarks like COCO-Stuff, Pascal-VOC, and Pascal-Context.
  arXiv  Detail & Related papers  (2024-06-02T08:32:51Z)
• CLIP Can Understand Depth [6.877245323116022]
  We show that CLIP can be adapted to downstream tasks where its vision-language alignment is suboptimally learned during pre-training on web-crawled data.<n>We distill the semantic prior of its frozen text encoder into a single learnable embedding matrix called "mirror"<n>The resulting model exhibits impressive performance, matching several state-of-the-art vision models on the NYU Depth v2 and KITTI benchmark datasets.
  arXiv  Detail & Related papers  (2024-02-05T18:09:33Z)
• Symmetrical Linguistic Feature Distillation with CLIP for Scene Text Recognition [77.93678598476149]
  We establish a novel Symmetrical Linguistic Feature Distillation framework (named CLIP-OCR) By cascading the CLIP image encoder with the reversed CLIP text encoder, a symmetrical structure is built with an image-to-text feature flow. Extensive experiments demonstrate the effectiveness of CLIP-OCR with 93.8% average accuracy on six popular STR benchmarks.
  arXiv  Detail & Related papers  (2023-10-08T04:00:20Z)
• Towards Realistic Zero-Shot Classification via Self Structural Semantic Alignment [53.2701026843921]
  Large-scale pre-trained Vision Language Models (VLMs) have proven effective for zero-shot classification. In this paper, we aim at a more challenging setting, Realistic Zero-Shot Classification, which assumes no annotation but instead a broad vocabulary. We propose the Self Structural Semantic Alignment (S3A) framework, which extracts structural semantic information from unlabeled data while simultaneously self-learning.
  arXiv  Detail & Related papers  (2023-08-24T17:56:46Z)
• CAILA: Concept-Aware Intra-Layer Adapters for Compositional Zero-Shot Learning [14.496173899477283]
  We study the problem of Compositional Zero-Shot Learning (CZSL), which is to recognize novel attribute-object combinations with pre-existing concepts. We propose to insert adapters, a parameter-efficient technique proven to be effective among large language models, into each CLIP encoder layer. We further equip adapters with concept awareness so that concept-specific features of "object", "attribute", and "composition" can be extracted.
  arXiv  Detail & Related papers  (2023-05-26T07:02:57Z)
• CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model [55.321010757641524]
  We introduce CLIP4STR, a simple yet effective STR method built upon image and text encoders of CLIP.<n>We scale CLIP4STR in terms of the model size, pre-training data, and training data, achieving state-of-the-art performance on 13 STR benchmarks.
  arXiv  Detail & Related papers  (2023-05-23T12:51:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.