visiononly

Visiononly refers to systems, methods, or approaches that rely exclusively on visual information for perception, understanding, or decision making, excluding data from other sensory modalities such as audio or tactile feedback. In artificial intelligence and computer vision, vision-only models process images or video frames to infer semantics, geometry, or actions without multimodal inputs.

Typical tasks include image classification, object detection, segmentation, depth estimation from monocular cues, motion tracking, and 3D reconstruction. Common architectures range from convolutional neural networks to vision transformers, often trained on large image or video datasets such as ImageNet or COCO. Some approaches use self-supervised learning to exploit unlabeled visual data.

Vision-only systems differ from multimodal systems, which fuse information from multiple senses to improve robustness and context. Vision-only approaches are simpler and can offer privacy advantages, but can suffer from ambiguity in lighting or occlusion, and may be vulnerable to adversarial examples that exploit visual cues. They may also struggle with tasks requiring contextual or tactile information.

Applications appear in robotics, autonomous navigation, surveillance, medical imaging, quality inspection, and augmented/virtual reality where visual cues are primary or sufficient for task execution. Limitations include sensitivity to lighting and viewpoint, lack of tactile feedback for manipulation, and challenges in grounding vision in physical constraints. Research directions include improving depth perception from monocular cues, robust visual localization, and evaluating generalization across datasets.

Historically, vision-only approaches have been foundational in computer vision and continue to play a central role even as multimodal AI grows. Benchmarks such as ImageNet, COCO, and Kinetics evaluate vision-only capabilities, while ongoing work examines how vision-driven systems interact with other modalities.