A New Direction in Visual Transformers: Predicting the Next Lines of an Image
In recent years, LLMs and Transformers have revolutionized natural language understanding through self-supervised learning — predicting the next tokens in a sequence. I’ve been exploring how a similar principle can be applied to images, not by labels or masks, but by predicting the next few lines of an image from the previous ones, using specialized attention patterns.
The Core Idea
Instead of classifying or labeling entire images, the model processes an image line by line — just as GPT reads text. Each line (or small patch row) becomes a “visual token.” The model learns to predict what the next few lines should look like, based only on the previous context.
By doing so, the model develops a deep understanding of structure, edges, and textures — completely self-supervised, no human labels required.
Why It Matters
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Label-Free Training: No need for expensive medical or manual annotations. The data itself becomes its own teacher.
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Rich Structural Learning: Predicting future image lines enforces continuity and context awareness.
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Adaptive Attention: Different attention heads can specialize — vertical, horizontal, or diagonal — to learn directional dependencies more efficiently.
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Medical Imaging Applications: For CT or MRI scans, missing or abnormal tissue patterns will produce high reconstruction errors, naturally flagging anomalies like tumors.
Related Works and Inspiration
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PixelRNN and ImageGPT showed that images can be treated as sequences.
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Axial Transformers introduced row–column attention, which aligns with this idea.
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Masked Autoencoders (MAE) and Models Genesis in medical imaging proved self-supervised reconstruction can match or beat supervised models.
My proposal unifies these ideas: 
Autoregressive prediction of image lines + directional attentions = interpretable, efficient, and domain-adaptable vision model.
Next Step
The plan is to:
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Train a small Vision Transformer that processes images row by row.
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Add separate attention heads for horizontal and vertical continuity.
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Evaluate reconstruction quality and anomaly detection on public medical datasets (e.g., Chest X-ray, Brain MRI).
Even a simple implementation could reveal how much structure a Transformer can infer purely from spatial continuity.
1. Visual Prediction as Driving Context
Instead of predicting the next lines of a static image, the model can predict the next visual segments of the road from the camera’s current view.
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Each incoming camera frame (from the windshield or dash camera) becomes a sequence of image lines or patches.
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The Transformer predicts how those lines will continue — i.e., where the lane markings, road boundaries, or objects will appear in the next frame.
This gives the vehicle predictive spatial awareness — seeing where lines should be even if they are faded, covered by shadows, or temporarily lost.
2. How It Works
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Input: Recent image frames (past few seconds).
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Model: Line-based or patch-based Vision Transformer with directional attention heads (forward, lateral, curvature).
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Output: Predicted geometry of road lines and direction flow (left/right turn, lane merging, road edge continuation).
That’s not simple lane detection — it’s temporal autoregression of the road scene.
3. Benefits
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Robust in poor visibility: When rain, glare, or faded paint obscure lanes, the model can infer missing parts based on previous structure.
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Self-supervised training: It can train on unlabeled driving videos — the next frame itself is the training target. No image segmentation is needed.
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Predictive planning: Coupled with steering and speed control, it can anticipate curves or road splits earlier.
4. Related Work
A few research threads overlap, but none are identical to your formulation:
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VideoGPT / MaskViT – frame-to-frame prediction using transformers.
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Wayformer (Google DeepMind) – multimodal Transformer for motion prediction.
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BEVFormer (2023) – transforms multi-camera features into bird’s-eye view, but not line-based prediction.
Closing Thought
When GPT predicts the next word, it learns the logic of language. When a Transformer predicts the next lines of an image and driving context, it can learn the logic of vision. That’s the essence of self-supervised intelligence — learning not from labels, but from the structure of reality itself.