Common Visual Challenges
While vision models are impressive, they face several systematic challenges that are important to understand when building applications. These limitations stem from how vision models process images—through patches, embeddings, and attention—rather than the way humans perceive visual information.
VLM Failure Mode Explorer
Interactive scenarios showing where vision models struggle
Object Counting Challenge
A photo of a desk with scattered paperclips. There are exactly 23 paperclips visible, some overlapping each other.
"I can see approximately 15-20 paperclips scattered across the desk."
Key Insight
VLMs are powerful but not infallible. Understanding their systematic weaknesses helps you design robust applications that leverage their strengths while mitigating their limitations.
Counting Objects
Models often struggle to accurately count objects in images, especially when there are many similar items.
Why This Happens
Vision models process images as patches (typically 14x14 or 16x16 pixels), not as discrete objects. They lack the built-in concept of "object permanence" and struggle to maintain accurate counts across overlapping or dense arrangements.
Common Failures
- •Counting people in a crowd (often off by 20-50%)
- •Counting items in a grid or array
- •Distinguishing between "few" and "many" when items overlap
Workarounds
For critical counting tasks, consider using specialized object detection models (YOLO, Faster R-CNN) or asking the model to identify and describe each item individually rather than providing a total count.
Spatial Reasoning
Understanding precise spatial relationships between objects (left/right, above/below) can be unreliable.
Why This Happens
Positional information is encoded through patch position embeddings, but these don't provide pixel-level precision. The model learns statistical correlations between positions rather than explicit spatial reasoning.
Common Failures
- •Confusing left/right relationships in mirrored or symmetric images
- •Misjudging relative distances ("closer to" or "farther from")
- •Difficulty with rotated or unusual orientations
Workarounds
Be explicit in your prompts about which reference frame to use. Consider annotating images with visual markers or grids for critical spatial tasks.
Small Text Recognition
Fine text in images may be misread or missed entirely, especially at low resolutions.
Why This Happens
Text smaller than the patch size (14-16 pixels) gets compressed into a single embedding, losing character-level detail. OCR is not built into vision LLMs—they learn text recognition as a byproduct of training, not as a dedicated capability.
Common Failures
- •Misreading license plates, street signs, or small labels
- •Confusing similar characters (0/O, 1/l/I, 5/S)
- •Missing text in busy or low-contrast backgrounds
Workarounds
Use high-resolution images and zoom in on text regions. For critical OCR tasks, use dedicated OCR tools (Tesseract, Google Vision API, Amazon Textract) alongside or instead of vision LLMs.
Visual Hallucination
Models may describe objects or details that aren't actually present in the image.
Why This Happens
Vision LLMs are trained to generate plausible descriptions. When image features are ambiguous, the model fills in gaps with statistically likely content—even if that content isn't in the image. This is the same mechanism that causes text hallucination.
Common Failures
- •Adding objects that "should" be in a scene (a keyboard near a monitor)
- •Describing brand names or text that isn't visible
- •Inventing details when asked about unclear regions
Workarounds
Ask the model to express uncertainty. Use prompts like "describe only what you can clearly see" or "if you cannot determine X, say so." Cross-reference critical details.
Fine Detail Recognition
Subtle details, textures, or small distinguishing features are often missed or misidentified.
Why This Happens
The patch-based architecture averages information within each patch, losing fine-grained detail. High-frequency visual information (edges, textures, small features) is compressed.
Common Failures
- •Distinguishing between similar objects (dog breeds, car models)
- •Reading gauges, meters, or instrument displays
- •Identifying subtle damage or defects in inspection tasks
Workarounds
Use the highest resolution available. Crop and focus on specific regions of interest. For specialized tasks, consider fine-tuned models trained on domain-specific data.
Multi-Image Reasoning
Comparing or reasoning across multiple images is significantly harder than single-image tasks.
Why This Happens
Each image is encoded separately into token sequences. Cross-image attention must happen through the language model's context window, which is less efficient than dedicated multi-image architectures.
Common Failures
- •Finding differences between two similar images ("spot the difference")
- •Tracking object identity across frames
- •Comparing fine details between product images
Workarounds
Describe each image separately first, then ask for comparison. Consider combining images into a single composite for direct comparison.
Key Takeaways
- 1Vision LLMs process images as patches—detail below patch resolution is lost
- 2Counting and spatial reasoning are fundamental weaknesses, not edge cases
- 3Visual hallucination follows the same pattern as text hallucination—plausible fabrication
- 4Use higher resolution, cropped regions, and explicit prompts to improve accuracy
- 5For critical tasks, combine vision LLMs with specialized tools (OCR, object detection)
- 6Always verify important visual information through other means