VOCAL: Visual Odometry via ContrAstive Learning

• URL: http://arxiv.org/abs/2507.00243v1
• Date: Mon, 30 Jun 2025 20:26:13 GMT
• Title: VOCAL: Visual Odometry via ContrAstive Learning
• Abstract summary: We introduce VOCAL (Visual Odometry via ContrAstive Learning), a novel framework that reimagines visual odometry as a label ranking challenge.<n>By integrating Bayesian inference with a representation learning framework, VOCAL organizes visual features to mirror camera states.<n>This strategic alignment bolsters the interpretability of the learned features and ensures compatibility with multimodal data sources.
• Score: 25.50526976366299
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Breakthroughs in visual odometry (VO) have fundamentally reshaped the landscape of robotics, enabling ultra-precise camera state estimation that is crucial for modern autonomous systems. Despite these advances, many learning-based VO techniques rely on rigid geometric assumptions, which often fall short in interpretability and lack a solid theoretical basis within fully data-driven frameworks. To overcome these limitations, we introduce VOCAL (Visual Odometry via ContrAstive Learning), a novel framework that reimagines VO as a label ranking challenge. By integrating Bayesian inference with a representation learning framework, VOCAL organizes visual features to mirror camera states. The ranking mechanism compels similar camera states to converge into consistent and spatially coherent representations within the latent space. This strategic alignment not only bolsters the interpretability of the learned features but also ensures compatibility with multimodal data sources. Extensive evaluations on the KITTI dataset highlight VOCAL's enhanced interpretability and flexibility, pushing VO toward more general and explainable spatial intelligence.

Related papers

• Towards Generalizable Visually Grounded Exploration of Household Devices [74.66117923766632]
  Key bottleneck of realizing general autonomous embodied agents lies in Generalizable Visually Grounded Exploration.<n>We introduce VGEBench, a benchmark designed to evaluate the generalizable visually grounded exploration capabilities of Vision-Language Models.<n>Unlike static datasets, we construct a Logic-Driven State Machine framework. This framework simulates multi-turn interaction loops, compelling agents to achieve goals by active visual perception and feedback-driven correction.
  arXiv  Detail & Related papers  (2026-09-01T07:42:45Z)
• ViQ: Text-Aligned Visual Quantized Representations at Any Resolution [91.46185855575789]
  We present ViQ, a Visual Quantized Representations framework, which is designed to balance semantics and details in discrete representations.<n>Our approach structures quantization learning into two stages: text-aligned pre-training and feature discretization.<n>Experiments on multimodal tasks demonstrate that ViQ achieves competitive performance compared to state-of-the-art multimodal vision encoders.
  arXiv  Detail & Related papers  (2026-06-25T17:29:27Z)
• SpatialImaginer: Towards Adaptive Visual Imagination for Spatial Reasoning [67.67774742200626]
  spatial intelligence, which refers to the ability to reason about geometric and physical structure from visual observations, is a core challenge for large language models.<n>We propose a unified multimodal generation framework that integrates textual reasoning with visual imagination.<n>Our framework adopts a divide-and-conquer strategy, using text chain-of-thought for high-level semantic planning and the visual imagination for geometry-sensitive state transformation and consistency preservation.
  arXiv  Detail & Related papers  (2026-04-19T11:21:59Z)
• Revisiting Multi-Task Visual Representation Learning [52.93947931352643]
  We introduce MTV, a principled multi-task visual pretraining framework.<n>We leverage high-capacity "expert" models to synthesize dense, structured pseudo-labels at scale.<n>Our results demonstrate that MTV achieves "best-of-both-worlds" performance.
  arXiv  Detail & Related papers  (2026-01-20T11:59:19Z)
• The Perceptual Observatory Characterizing Robustness and Grounding in MLLMs [44.71703930770065]
  We present The Perceptual Observatory, a framework that characterizes MLLMs across verticals like face matching and text-in-vision comprehension capabilities.<n>The Perceptual Observatory moves beyond leaderboard accuracy to yield insights into how MLLMs preserve perceptual grounding and relational structure under perturbations.
  arXiv  Detail & Related papers  (2025-12-17T20:22:23Z)
• Chain-of-Visual-Thought: Teaching VLMs to See and Think Better with Continuous Visual Tokens [54.18057944158818]
  Chain-of-Visual-Thought (COVT) is a framework that enables Vision-Language Models (VLMs) to reason through continuous visual tokens.<n>Within a small budget of roughly 20 tokens, COVT distills knowledge from lightweight vision experts.<n>During training, the VLM with COVT autoregressively predicts visual tokens to reconstruct dense supervision signals.
  arXiv  Detail & Related papers  (2025-11-24T18:55:19Z)
• How Far are VLMs from Visual Spatial Intelligence? A Benchmark-Driven Perspective [103.44502230776352]
  We present a systematic investigation of Visual Spatial Reasoning (VSR) in Vision-Language Models (VLMs)<n>We categorize spatial intelligence into three levels of capability, ie, basic perception, spatial understanding, spatial planning, and curate SIBench, a spatial intelligence benchmark encompassing nearly 20 open-source datasets across 23 task settings.
  arXiv  Detail & Related papers  (2025-09-23T12:00:14Z)
• Beyond Transcription: Mechanistic Interpretability in ASR [26.551400592078213]
  Interpretability methods have recently gained significant attention, particularly in the context of large language models.<n>We adapt and apply established interpretability methods to examine how acoustic and semantic information evolves across layers in ASR systems.<n>Our experiments reveal previously unknown internal dynamics, including specific encoder-decoder interactions responsible for repetition hallucinations and semantic biases encoded deep within acoustic representations.
  arXiv  Detail & Related papers  (2025-08-21T15:42:53Z)
• Implicit Counterfactual Learning for Audio-Visual Segmentation [50.69377287012591]
  We propose the implicit counterfactual framework (ICF) to achieve unbiased cross-modal understanding.<n>Due to the lack of semantics, heterogeneous representations may lead to erroneous matches.<n>We introduce the multi-granularity implicit text (MIT) involving video-, segment- and frame-level as the bridge to establish the modality-shared space.
  arXiv  Detail & Related papers  (2025-07-28T11:46:35Z)
• CS-VLM: Compressed Sensing Attention for Efficient Vision-Language Representation Learning [0.0]
  We introduce the Compressed Sensing Attention Transformer (CSAT), a novel architecture that reimagines attention computation through the lens of compressed sensing.<n>CSAT exploits the inherent compressibility of both visual and textual representations especially evident in video, where temporal redundancy is high, and in language, where cross-modal grounding is often sparse.
  arXiv  Detail & Related papers  (2025-06-30T02:11:20Z)
• From Data to Modeling: Fully Open-vocabulary Scene Graph Generation [29.42202665594218]
  OvSGTR is a transformer-based framework for fully open-vocabulary scene graph generation.<n>Our approach jointly predicts objects (nodes) and their inter-relationships (edges) beyond predefined categories.
  arXiv  Detail & Related papers  (2025-05-26T15:11:23Z)
• AVadCLIP: Audio-Visual Collaboration for Robust Video Anomaly Detection [57.649223695021114]
  We present a novel weakly supervised framework that leverages audio-visual collaboration for robust video anomaly detection.<n>Our framework demonstrates superior performance across multiple benchmarks, with audio integration significantly boosting anomaly detection accuracy.
  arXiv  Detail & Related papers  (2025-04-06T13:59:16Z)
• Text Speaks Louder than Vision: ASCII Art Reveals Textual Biases in Vision-Language Models [93.46875303598577]
  Vision-language models (VLMs) have advanced rapidly in processing multimodal information, but their ability to reconcile conflicting signals remains underexplored.<n>This work investigates how VLMs process ASCII art, a unique medium where textual elements collectively form visual patterns, potentially creating semantic-visual conflicts.
  arXiv  Detail & Related papers  (2025-04-02T10:47:07Z)
• Integrating Frequency-Domain Representations with Low-Rank Adaptation in Vision-Language Models [0.6715525121432597]
  This research presents a novel vision language model (VLM) framework to enhance feature extraction, scalability, and efficiency.<n>We evaluate the proposed model on caption generation and Visual Question Answering (VQA) tasks using benchmark datasets with varying levels of Gaussian noise.<n>Our model provides more detailed and contextually relevant responses, particularly for real-world images captured by a RealSense camera mounted on an Unmanned Ground Vehicle (UGV)
  arXiv  Detail & Related papers  (2025-03-08T01:22:10Z)
• SPHERE: Unveiling Spatial Blind Spots in Vision-Language Models Through Hierarchical Evaluation [7.659514491338669]
  Current vision-language models may grasp basic spatial cues but struggle with the multi-dimensional spatial reasoning necessary for human-like understanding and real-world applications.<n>We develop SPHERE, a hierarchical evaluation framework supported by a new human-annotated dataset.<n> Benchmark evaluation of state-of-the-art models reveals significant deficiencies, especially in reasoning about distance and proximity.
  arXiv  Detail & Related papers  (2024-12-17T09:10:55Z)
• Heterogeneous Space Fusion and Dual-Dimension Attention: A New Paradigm for Speech Enhancement [7.789114492151524]
  We introduce a novel speech enhancement framework, HFSDA, which integrates heterogeneous spatial features and incorporates a dual-dimension attention mechanism. Our model excels at capturing both high-level semantic information and detailed spectral data, enabling a more thorough analysis and refinement of speech signals. We refine the Conformer model by enhancing its feature extraction capabilities not only in the temporal dimension but also across the spectral domain.
  arXiv  Detail & Related papers  (2024-08-13T14:04:24Z)
• SignVTCL: Multi-Modal Continuous Sign Language Recognition Enhanced by Visual-Textual Contrastive Learning [51.800031281177105]
  SignVTCL is a continuous sign language recognition framework enhanced by visual-textual contrastive learning. It integrates multi-modal data (video, keypoints, and optical flow) simultaneously to train a unified visual backbone. It achieves state-of-the-art results compared with previous methods.
  arXiv  Detail & Related papers  (2024-01-22T11:04:55Z)
• Perceiver-VL: Efficient Vision-and-Language Modeling with Iterative Latent Attention [100.81495948184649]
  We present Perceiver-VL, a vision-and-language framework that efficiently handles high-dimensional multimodal inputs such as long videos and text. Our framework scales with linear complexity, in contrast to the quadratic complexity of self-attention used in many state-of-the-art transformer-based models.
  arXiv  Detail & Related papers  (2022-11-21T18:22:39Z)
• Towards Scale Consistent Monocular Visual Odometry by Learning from the Virtual World [83.36195426897768]
  We propose VRVO, a novel framework for retrieving the absolute scale from virtual data. We first train a scale-aware disparity network using both monocular real images and stereo virtual data. The resulting scale-consistent disparities are then integrated with a direct VO system.
  arXiv  Detail & Related papers  (2022-03-11T01:51:54Z)

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.