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cs.CV updates on arXiv.org

ClawGUI: A Unified Framework for Training, Evaluating, and Deploying GUI Agents On the Robustness of Watermarking for Autoregressive Image Generation Revisiting Compositionality in Dual-Encoder Vision-Language Models: The Role of Inference Anthropogenic Regional Adaptation in Multimodal Vision-Language Model From Redaction to Restoration: Deep Learning for Medical Image Anonymization and Reconstruction The Salami Slicing Threat: Exploiting Cumulative Risks in LLM Systems BoxTuning: Directly Injecting the Object Box for Multimodal Model Fine-Tuning Semantic-Geometric Dual Compression: Training-Free Visual Token Reduction for Ultra-High-Resolution Remote Sensing Understanding Lightweight Low-Light Image Enhancement via Distribution-Normalizing Preprocessing and Depthwise U-Net Back to the Barn with LLAMAs: Evolving Pretrained LLM Backbones in Finetuning Vision Language Models Pseudo-Unification: Entropy Probing Reveals Divergent Information Patterns in Unified Multimodal Models QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits Evaluating the Impact of Medical Image Reconstruction on Downstream AI Fairness and Performance Retinal Cyst Detection from Optical Coherence Tomography Images LoViF 2026 The First Challenge on Weather Removal in Videos STORM: End-to-End Referring Multi-Object Tracking in Videos Data-Efficient Surgical Phase Segmentation in Small-Incision Cataract Surgery: A Controlled Study of Vision Foundation Models Rethinking the Diffusion Model from a Langevin Perspective Zero-shot World Models Are Developmentally Efficient Learners Edu-MMBias: A Three-Tier Multimodal Benchmark for Auditing Social Bias in Vision-Language Models under Educational Contexts VGA-Bench: A Unified Benchmark and Multi-Model Framework for Video Aesthetics and Generation Quality Evaluation Degradation-Consistent Paired Training for Robust AI-Generated Image Detection FREE-Switch: Frequency-based Dynamic LoRA Switch for Style Transfer Demographic and Linguistic Bias Evaluation in Omnimodal Language Models FlowPalm: Optical Flow Driven Non-Rigid Deformation for Geometrically Diverse Palmprint Generation Cross-Cultural Value Awareness in Large Vision-Language Models I Walk the Line: Examining the Role of Gestalt Continuity in Object Binding for Vision Transformers GLEaN: A Text-to-image Bias Detection Approach for Public Comprehension From UAV Imagery to Agronomic Reasoning: A Multimodal LLM Benchmark for Plant Phenotyping Not Your Stereo-Typical Estimator: Combining Vision and Language for Volume Perception
DIJIT: A Robotic Head for an Active Observer
Mostafa Kamali Tabrizi, Mingshi Chi, Bir Bikram Dey, Kelly Yuan, · 2025-12-09 · via cs.CV updates on arXiv.org

We present DIJIT, a novel binocular robotic head expressly designed for mobile agents that behave as active observers. DIJIT's unique breadth of functionality enables active vision research and the study of human-like eye and head-neck motions, their interrelationships, and how each contributes to visual ability. DIJIT is also being used to explore the differences between how human vision employs eye/head movements to solve visual tasks and current computer vision methods. DIJIT's design features nine mechanical degrees of freedom, while the cameras and lenses provide an additional four optical degrees of freedom. The ranges and speeds of the mechanical design are comparable to human performance. DIJIT attains 85\% of the peak human saccade speed. Our design includes the ranges of motion required for convergent stereo, namely, vergence, version, and cyclotorsion. Here, we present DIJIT and some aspects of its performance. We also present a novel method for saccadic camera movements, using a direct relationship between camera orientation and motor values. The resulting saccadic camera movements are close to human movements in terms of their accuracy, with 1.17$^\circ$ and 1.14$^\circ$ mean error for the left and right cameras, respectively.