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SCOPS: Self-Supervised Co-Part Segmentation
Wei-Chih Hung, Varun Jampani, Sifei Liu, Pavlo Molchanov, Ming-Hsuan Yang, Jan Kautz
TL;DR
Part segmentation is costly to annotate and often fails to generalize across object categories and highly variable instances. SCOPS learns category-agnostic, semantically consistent parts through self-supervised loss functions, and experiments show sharper boundary-aligned segments and favorable performance against existing methods. Its evaluation is limited by symmetric-part ambiguity in the CUB landmark-based assessment.
Problem
Manual part-segmentation annotations are costly and category-specific, limiting generalization to unseen object classes amid substantial object variation.
Method
SCOPS uses self-supervised loss functions encoding geometric concentration, equivariance, semantic consistency, and saliency constraints to learn part segments from image collections.
Results
SCOPS produces semantically consistent, boundary-adhering part segments, remains robust to appearance, viewpoint, pose, and occlusion variations, and performs favorably against existing methods.
Takeaways & Limitations
Part segmentation provides a richer intermediate representation than landmarks or bounding boxes while remaining usable for standalone-image inference.
Takeaways & Limitations
On CUB, symmetric semantic parts are often not distinguished, making landmark-regression error an imperfect measure of co-part segmentation quality.
Abstract
from arXiv · showhide
Parts provide a good intermediate representation of objects that is robust with respect to the camera, pose and appearance variations. Existing works on part segmentation is dominated by supervised approaches that rely on large amounts of manual annotations and can not generalize to unseen object categories. We propose a self-supervised deep learning approach for part segmentation, where we devise several loss functions that aids in predicting part segments that are geometrically concentrated, robust to object variations and are also semantically consistent across different object instances. Extensive experiments on different types of image collections demonstrate that our approach can produce part segments that adhere to object boundaries and also more semantically consistent across object instances compared to existing self-supervised techniques.
1. Introduction
SCOPS addresses costly, category-specific supervision by learning semantically consistent part segmentations from image collections using self-supervised loss functions. The predicted parts are designed to remain geometrically coherent and robust to object, camera, pose, and appearance variations.
- Manual part-segmentation annotations are costly, category-specific, and difficult to generalize to unseen object classes.
- SCOPS learns part segmentations from same-category image collections using very weak supervision and is applicable to rigid or non-rigid object categories.
- Its loss functions encode geometric concentration, robustness to variations, semantic consistency, and the requirement that parts form objects rather than background.
- SCOPS produces semantically consistent part segments across object instances despite substantial appearance and pose variability.
- Compared with unsupervised landmark methods, SCOPS is relatively robust to appearance variations, handles occlusions, and supports multiple object instances in an image.
- Compared with DFF, SCOPS scales to larger datasets, produces sharper boundary-adhering segments, and achieves greater semantic consistency across instances.
2. Related Works
Related work includes methods for interpreting CNN features, discovering landmarks, aligning images densely, and co-segmenting foregrounds. SCOPS differs by targeting richer part segmentations and single-image inference under less restrictive settings.
- Object concept discovery and CNN-interpretation studies motivate learning object-part information from pretrained representations.
- Weakly or unsupervised landmark methods use geometric constraints but often assume cropped single-object images and minor occlusions.
- Dense alignment methods match pixels or landmarks across instances, while some weakly supervised approaches assume affine or thin-plate-spline transformations.
- Co-segmentation methods jointly process image collections at inference, whereas SCOPS takes a single image and outputs part segmentation.
3. Self-Supervised Co-Part Segmentation
SCOPS learns part segmentations from single images using differentiable geometric, equivariance, semantic-consistency, and saliency constraints. The framework targets concentrated parts, robustness to transformations, cross-instance semantic consistency, and foreground-focused segments.
- 3.1. Overall Framework: SCOPS trains a fully convolutional network to predict K part-response maps plus a background channel from a single image.The network is trained on image collections from the same object category without ground-truth segmentation annotations.
- 3.2. Geometric Concentration Loss: Geometric concentration penalizes part responses distant from their spatial center, encouraging compact part segments.The loss minimizes the variance of each part’s spatial probability distribution and differs from landmark diversity losses that separate responses.
- 3.3. Equivariance Loss: Equivariance compares predictions for an image and its spatially transformed, color-jittered version, enforcing consistency of both segmentations and part centers.The method uses random similarity transformations; more complex projective and thin-plate-spline transformations did not improve segmentation.
- 3.4. Semantic Consistency Loss: Semantic consistency uses globally shared part-basis vectors over pretrained intermediate CNN features to align corresponding parts across object instances.The shared basis explicitly connects similar response maps with similar embeddings in the pretrained feature space.
- 3.4. Semantic Consistency Loss: Orthonormal and saliency constraints distinguish part bases and suppress background features, encouraging parts to correspond to foreground objects.Orthonormalization reduces correlations between basis vectors, while saliency masking projects non-salient background regions away from the learned subspace.
4. Experiments
SCOPS is evaluated on faces, birds, and common objects using proxy landmark and foreground-segmentation measures, alongside qualitative and ablation analyses. Across these settings, it generally compares favorably with existing techniques, while symmetric bird parts expose a limitation of the landmark proxy.
- Evaluation setup: SCOPS is evaluated with landmark regression on CelebA, AFLW, and CUB, and foreground segmentation IoU on PASCAL VOC.These proxy measures assess learned part segmentations across human faces, birds, and common objects.
- Faces from unaligned CelebA/AFLW: SCOPS performs favorably against ULD and DFF on unaligned CelebA and AFLW images.On CelebA, ULD struggles to converge to semantically meaningful landmarks under unaligned training and testing.
- Ablation study: Removing any loss constraint worsens performance, with semantic consistency loss producing the largest ablation drop.Visualizations indicate that without semantic consistency loss, the learned parts lack semantic meaning.
- Birds from CUB: SCOPS performs favorably against ULD and DFF on the three evaluated CUB bird categories.The CUB images include substantial pose and camera-viewpoint variation, including standing, swimming, and flying birds.
- Evaluation limitation: On CUB, symmetric left-right parts are often assigned the same segment, making landmark regression error an imperfect measure of co-part segmentation quality.The authors identify distinguishing symmetric semantic parts as a continuing challenge in this dataset.
- Common objects from PASCAL: SCOPS outperforms DFF in PASCAL VOC foreground IoU, with and without CRF post-processing.The visual results also show robustness to appearance and pose articulations, although IoU is only indicative of part-segmentation quality.
5. Concluding Remarks
SCOPS is presented as a self-supervised framework for learning semantically consistent co-part segmentations without ground-truth annotations. Its loss design targets geometric configuration, transformation robustness, and cross-instance correspondence, and experiments report favorable comparisons with existing methods.
- Conclusion: SCOPS learns semantically consistent part segmentations from object-category image collections without ground-truth annotations.The method is intended as a general framework for co-part segmentation across object appearances, viewpoints, and pose articulations.
- Conclusion: The training objective combines geometric concentration, equivariance, semantic consistency, and orthonormal constraints.The final objective is a linear combination of the corresponding loss functions.
- Implementation: The experiments use loss weights (λc, λeqv, λsc, λot) = (0.1, 10, 100, 0.1).These coefficients were selected through a coarse grid search on a subset of CelebA images.
B. Implementation Details of Equivariance Loss
SCOPS applies spatial and color perturbations to train for equivariance, and reports performance gains that remain robust across a wide range of perturbation parameters.
- Equivariance perturbations: Spatial transformations include random rotations, shifts, scaling, and thin-plate-spline warps, while color transformations jitter brightness, contrast, saturation, and hue.The reported spatial ranges include ±60° rotation, ±20% shifting, 0.3x−2x scaling, and ±10% TPS control-point shifts.
- Equivariance perturbations: Performance gains remain robust to a wide range of perturbation parameters.
C.1. On Using Different Part Number K
SCOPS performs best with a moderate number of parts: increasing K improves landmark robustness to face-pose variation, but gains saturate after K = 8 and extra parts become semantically unclear.
- Higher K produces more pose-robust landmark centers and lower landmark estimation error on unaligned CelebA.The evaluation considers K = 2, 4, 6, 8, and 10.
- Performance appears to saturate after K = 8.
- At K = 8 and K = 10, additional parts do not correspond to semantically meaningful face regions.
- Training progression shows improving part segmentations and grouping of pixels with similar ImageNet features into the same segmentation class.
D. Quantitative Results on iCoseg
SCOPS is additionally evaluated for foreground segmentation on five iCoseg image sets, where aggregating its predicted parts produces favorable cosegmentation performance against existing methods.
- SCOPS performs favorably against existing methods on foreground segmentation IoU across five iCoseg image sets.The model is trained with K = 4, and predicted parts are aggregated before evaluation.
E. Qualitative Results on PASCAL Objects
On PASCAL images, SCOPS produces part segments that remain consistent across rigid and non-rigid object instances despite appearance, pose, and camera-viewpoint variation.
- SCOPS produces consistent part segments across PASCAL instances with appearance, pose, and camera-viewpoint variation.
- The qualitative results cover both rigid objects and non-rigid objects.Figures 10–13 show rigid objects, while Figures 14–16 show non-rigid objects.
F. Additional Results on Unaligned CelebA and CUB
Additional visual results show that SCOPS handles pose and camera variation on CelebA and CUB while producing sharper boundaries, with further examples across multiple PASCAL object classes and training settings.
- SCOPS is robust to pose and camera variations on unaligned CelebA and CUB images.
- SCOPS has better boundary adherence than other techniques in the additional CelebA and CUB visual results.
- Training progression visualizes improving segmentations alongside ImageNet-feature pixels grouped by segmentation class.
- The PASCAL visual examples span cars, buses, aeroplanes, motorbikes, sheep, horses, and cows.
- Additional CelebA results compare SCOPS with existing techniques and show the effects of different loss constraints.