Can VLMs Learn Depth and Segmentation During Training but Use Only RGB at Inference?

An Architectural Question from Reddit

On Reddit, a developer raised a thought-provoking question: Can a vision-language model (VLM) simultaneously learn depth estimation and semantic segmentation during training, but rely solely on RGB images at inference? This is not a simple engineering detail but touches on the fundamental question of how multimodal models organize their internal representations. If feasible, it means the model can gain stronger spatial and semantic understanding without increasing inference cost; if not, it indicates an irreconcilable information conflict between auxiliary tasks and the main task.

This question remains at the theoretical stage, with no public systematic experimental conclusions yet. However, from the perspective of deep learning architecture design, we can break it down into several key technical dimensions.

Auxiliary Tasks as Regularization: Theoretical Feasibility

From a representation learning perspective, introducing depth and segmentation as auxiliary supervision signals during training is essentially a multi-task learning strategy. Depth estimation forces the model to understand the geometric structure of the scene, while semantic segmentation requires the model to distinguish object boundaries and categories. These two types of signals provide the visual encoder with richer gradient sources than pure RGB-text alignment during training.

Existing research has shown that auxiliary tasks in multi-task training can act as regularizers, helping the backbone network learn more generalizable features. Discarding the auxiliary task branches at inference and keeping only the RGB input path theoretically does not destroy the learned representations—because these representations have already been internalized in the encoder's weights. This is conceptually similar to "Learning Using Privileged Information" (LUPI), where additional information is used during training but only regular input at inference.

But there is a precondition: the auxiliary tasks must not compete with the main task for capacity, otherwise the model may allocate a large number of parameters to the depth/segmentation paths, weakening the RGB path's representations.

Learning Using Privileged Information (LUPI), proposed by Vapnik et al., has the core idea that during training, the model can access additional information (the "privileged information") that is unavailable or too costly at inference, and use this information to guide the learning of the main task. In the SVM framework, this is reflected in simultaneously optimizing the main task and privileged information constraints during training, leading to a better decision boundary. In deep learning, LUPI is usually implemented via auxiliary losses or knowledge distillation: the privileged information branch participates in forward and backward propagation during training, and is removed at inference, but the main network weights have already absorbed the inductive bias brought by the privileged information. The scenario envisioned in this article—"using depth/segmentation for training, RGB only at inference"—is a typical variant of LUPI, with the difference that the privileged information is not extra sensor data but dense supervision signals derivable from RGB. Understanding LUPI helps grasp the theoretical boundaries of this idea: privileged information must share low-level representations with the main task, and the auxiliary branch should not become a "shortcut" for the main task; otherwise, removing it at inference would cause a performance cliff.

The Real Difficulty: Modality Misalignment and Capacity Allocation

The "VLM" mentioned in the problem description is a vision-language model, whose core capability is cross-modal alignment—associating image regions with text semantics. Depth and segmentation belong to dense prediction tasks, where every pixel needs an output, whereas VLMs typically rely on global or region-level feature compression. These two types of tasks have an inherent tension in their requirements for feature granularity.

Specifically, the visual encoder of a VLM often adopts a ViT-like structure, outputting a sequence of patch-level features that are projected and aligned with text tokens. Depth estimation needs to preserve fine spatial position information, and segmentation needs to maintain boundary sharpness; these details are easily lost when compressing into the language space. If dense prediction heads are forcibly added during training, the encoder may be forced to retain more low-level details, which may not be beneficial for language alignment.

Another hidden risk is training-inference inconsistency. If the depth and segmentation branches participate in forward propagation during training and are suddenly removed at inference, the gradient distribution received by the encoder will change. Although the weights are already fixed, some feature channels that depend on the auxiliary paths may become "idle," causing wasted representation. This requires architectural design to ensure that the auxiliary branches are bypass-style rather than deeply coupled with the backbone.

Due to the lack of public experimental data, the actual impact of this capacity competition cannot be quantified at present. But it is certain that this is not a simple modification of "adding a head and running"; it requires careful design of gradient isolation and feature sharing mechanisms.

Dense prediction tasks and the global alignment objectives commonly used in VLMs are fundamentally different in computational paradigm. Dense prediction (such as depth estimation and semantic segmentation) requires the network to output independent predictions for each pixel or each patch, and the loss function is usually computed per pixel (e.g., L1, cross-entropy), requiring high-resolution feature maps. In contrast, cross-modal alignment in VLMs (such as CLIP-style contrastive learning) compresses the entire image into a single vector and computes cosine similarity with text vectors, naturally discarding spatial details. This difference leads to different properties of gradient signals: gradients from dense tasks are highly localized in space and have high variance, while gradients from contrastive learning are global and smooth. If jointly optimized on the same encoder, dense gradients may dominate low-level feature updates, biasing the encoder toward retaining texture and boundary information, which may not be useful or even introduce noise for the semantic abstraction of language alignment. In addition, the patch sequence of ViT has already reduced spatial resolution in deeper layers, and dense prediction heads often require upsampling or skip connections to recover details, further increasing the complexity of bypass design.

Application Value and Open Questions

If this idea holds, its application value is quite direct: no additional sensor cost at inference. It can be deployed with a pure RGB camera, while the model internally possesses geometric and segmentation awareness. This is particularly attractive for scenarios sensitive to latency and hardware cost, such as autonomous driving, robot manipulation, and AR/VR.

However, the Reddit discussion also left some unresolved questions: With what weight should the supervision signals of auxiliary tasks be injected? Is it necessary to gradually decay the auxiliary loss in the later stages of training to allow the model to smoothly transition to pure RGB inference? Should depth and segmentation be added simultaneously or introduced in stages? These questions require systematic ablation experiments to answer.

At present, the judgment that can be given is: the direction is theoretically sound, but the engineering threshold is not low. It is more suitable as a research topic for exploration first rather than a product solution for direct deployment. For developers interested in VLM representation learning, this is an open direction worth following.

One-line Summary

Using depth and segmentation as auxiliary supervision during VLM training and RGB only at inference is theoretically feasible, but capacity allocation and modality alignment are the core engineering challenges.

Paragraph Summary

This article explores an architectural question raised on Reddit: Can a vision-language model simultaneously learn depth estimation and semantic segmentation during training while relying only on RGB images at inference? The article argues for theoretical feasibility from the perspectives of multi-task learning and privileged information learning—auxiliary tasks can act as regularizers, and the representations they learn can be internalized into the encoder weights. However, the real difficulty lies in the fact that VLM cross-modal alignment relies on global or region-level feature compression, while depth and segmentation are dense prediction tasks with different feature granularity requirements, creating an inherent tension. In addition, auxiliary branches participating in forward propagation during training and being removed at inference may cause training-inference inconsistency and representation waste. The article suggests designing auxiliary branches as bypass structures and points out that questions such as auxiliary loss weights and introduction timing still require systematic ablation experiments. The direction is theoretically sound but has a non-trivial engineering threshold, making it more suitable as a research topic.