Learning Multiple Dense Prediction Tasks from Partially Annotated Data

CVPR 2022

Wei-Hong Li, Xialei Liu and Hakan Bilen


Despite the recent advances in multi-task learning of dense prediction problems, most methods rely on expensive labelled datasets. In this paper, we present a label efficient approach and look at jointly learning of multiple dense prediction tasks on partially annotated data (i.e. not all the task labels are available for each image), which we call multi-task partially-supervised learning. We propose a multi-task training procedure that successfully leverages task relations to supervise its multi-task learning when data is partially annotated. In particular, we learn to map each task pair to a joint pairwise task-space which enables sharing information between them in a computationally efficient way through another network conditioned on task pairs, and avoids learning trivial cross-task relations by retaining high-level information about the input image. We rigorously demonstrate that our proposed method effectively exploits the images with unlabelled tasks and outperforms existing semi-supervised learning approaches and related methods on three standard benchmarks.
Multi-task Partially-supervised Learning (MTPSL)

Multi-task Learning (MTL) [1][2][3][4] aims to perform multiple tasks within a single network. However, existing MTL methods require all training images to be labelled for all tasks to learn the MTL model from them (Fig. 1).

Figure 1. Fully annotated dataset.
Figure 2. Partially annotated dataset.

However, obtaining all the labels for each image requires very accurate synchronization between the sensors as collecting the dataset typically involves using multiple sensors to collect annotations for multiple tasks. So it is more common and realistic that the collected dataset is partially annotated. In other words, not all task labels are available in each training image (e.g. in Fig. 2, the image does not have label for task 2). To this end, we propose a more realistic and general setting for MTL, called multi-task partially-supervised learning, or MTPSL, and an architecture-agnostic algorithm for MTPSL.

Learning MTL Model from Partially Annotated Data
In MTPSL, given an image that is labelled for task 2 and it does not have label for task 1 (Fig. 3). Here, we discuss methods that learn MTL model from such partially annotated data.
Supervised Learning

A simple strategy (Supervised Learning) is to apply supervised loss on labelled tasks (Fig. 3). This learns MTL on all images but it cannot extract the task-specific information from the images for the unlabelled tasks.

Semi-supervised Learning

Alternatively, One can extend the Supervised Learning baseline by penalizing the inconsistent predictions of images over multiple perturbations for the unlabelled tasks (Semi-supervised Learning in Fig. 4), but it does not guarantee consistency across the related tasks. For example, if we are going to perform depth estimation and semantic segmentation and an area is wall in segmentation, then that area in depth should be flat.

Cross-task Consistency Learning

To this end, we propose to leverage cross-task consistency to supervise the learning on unlabelled task (Fig. 5).

Figure 3. Supervised Learning.
Figure 4. Semi-supervised Learning.
Figure 5. Cross-task Consistency Learning (Ours).
Cross-task Consistency Learning
Direct-Map [5][6]

To regularize the cross-task consistency, given the prediction of an unlabelled task and the ground-truth of a labelled task, one can use a mapping function to map prediction of the unlabelled task to labelled task's ground-truth space and align the mapped output with the groundtruth (Fig. 6). However, this direct-map strategy requires analytical derivation from task 1 to task 2 and it assumes task 2 can be recovered from task1.

Joint Space Mapping (Ours)

Instead of direct mapping, we propose to map both unlabelled task prediction and labelled task’s ground-truth to a taskpair joint space and regularize the cross-task consistency in the joint space (Fig. 7). This works for any related task pairs and learns only common patterns between task pairs, but naively modelling pairwise relations can be expensive and can result in trivial solutions.

Figure 6. Direct-Map.
Figure 7. Joint space mapping (Ours).
Regularized Conditional Cross-task Joint Space Mapping

To learn the joint space mapping, one can use separate mapping functions for mapping prediction and labels into the joint space. However, the number of taskpair mapping functions grows exponentially with the number of tasks. To address the problem, we propose to use a shared mapping conditioned on the taskpair information (Fig. 8). As shown in Fig. 8, the taskpair information is '\((1 \rightarrow 1,2)\)'.

Figure 8. Regularized Conditional Cross-task Joint Space Mapping.

However, the learning of the mappings can lead to trivial solution, for example, the outputs of mappings can be all zeros and the cross-task consistency loss would be zero. To prevent this, we propose to regularize the learning of mappings by aligning the outputs of mappings with a feature from the MTL model's feature encoder. This encourages the mapping to retain high-level information about the input image.

We evaluate our method on NYU-v2 [7], Cityscapes [8], Pascal-Context [9] under different settings. Please refer to our paper for more results.
Results on NYU-v2

Here, we show results on NYU-v2 which contains indoor images for three tasks: semantic segmentation, depth estimation and surface normal estimation. Table 1 depicts comparisons between Supervised Learning, Semi-supervised Learning and our method under the MTPSL setting where we randomly select and keep labels for 1 or 2 tasks in each image.

Table 1. Multi-task learning results on NYU-v2. Each image is annotated with a random number of task labels (1 or 2).
Qualitative results on NYU-v2

We also visualize the joint space feature map of the segmentation prediction and surface normal groundtruth at the right column in Fig. 9. The common information is around object boundaries which in turn enables the model to produce more accurate predictions for both tasks (Fig. 10).

Figure 9. Intermediate feature map of the mapping function of the task-pair (segmentation to surface normal) of one example in NYU-v2. The first column shows the prediction or ground-truth and the second column present the corresponding mapped feature map.
Figure 10. Qualitative results on NYU-v2. The fist column shows the RGB image, the second column plots the ground-truth or predictions with the IoU (\(\uparrow\)) score of all methods for semantic segmentation, the third column presents the ground-truth or predictions with the absolute error (\(\downarrow\)), and we show the prediction of surface normal with mean error (\(\downarrow\)) in the last column.

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