OmniTaskonomy Maps How Visual Generation Can Improve Visual Understanding

Introduction

Visual generation and visual understanding are often treated as separate branches of multimodal learning. A generation model produces an image or a structured visual output, while an understanding model may identify an object, estimate depth, locate a region, or answer a question in text. The paper OmniTaskonomy asks a more precise question: under what conditions can supervision for visual generation improve visual understanding?

The study, conducted by researchers including a team from UC Berkeley, does not assume that every generation objective is automatically useful. Instead, it compares controlled image-to-image (I2I) generation and image-to-text (I2T) understanding tasks that express the same underlying visual problem through different output modalities. The results indicate that I2I training can improve downstream I2T performance when the training recipe is appropriate. The gains also become larger as the amount of I2I training data increases.

Key findings

• Controlled task pairs make transfer easier to measure. By keeping the underlying visual problem comparable while changing the output format, the study separates the effect of generation supervision from unrelated task differences.
• OmniTaskonomy provides a broader transfer map. The proposed taxonomy covers 19 I2I generation tasks and 25 I2T understanding capabilities. Rather than asking whether generation is useful in general, it asks which generation task helps which understanding skill.
• Some transfers match intuition. Depth prediction improves metric 3D reasoning, object pointing helps counting, and jigsaw reconstruction benefits 2D ordering. These relationships suggest that generation objectives can encourage representations of geometry, spatial layout, and object structure.
• Other transfers are less obvious. The researchers report that 2.5D segmentation can improve category recognition, while Z-depth prediction can improve localization. The most useful source task is therefore not always the one with the most similar-looking output.
• Gradient alignment offers a possible explanation. When the gradients associated with a generation task and an understanding task are more strongly aligned, the downstream transfer tends to be larger. This provides a signal for selecting task combinations instead of relying only on surface-level intuition.

Why it matters

The paper reframes visual generation as a potential source of perceptual supervision, not merely a way to produce attractive images. A suitable generation objective may force a model to represent depth, object boundaries, spatial relations, or ordering, and those representations can later support recognition and reasoning.

At the same time, the findings argue against indiscriminate multitask training. Adding a generation loss is not guaranteed to improve understanding; the task pairing, curriculum, data scale, and optimization behavior all matter. OmniTaskonomy offers a way to reason about these choices at the task level and could guide future work on curriculum learning, multimodal pretraining, and foundation-model design.

The broader lesson is that visual generation and understanding are not isolated capabilities. Their relationship depends on what the model is asked to generate and how that objective interacts with the target understanding skill. Mapping those interactions may be as important as increasing model size or collecting more generic image-text data.

Source: Hugging Face Daily Papers