Source-linked AI summary
Joint Object and Part Segmentation using Deep Learned Potentials
Peng Wang, Xiaohui Shen, Zhe Lin, Scott Cohen, Brian Price, Alan Yuille
TL;DR
Sequential object-then-part segmentation can propagate object-mask errors into part labels, despite the two tasks offering complementary context. This paper jointly predicts shared semantic-part and object potentials, and reports improved performance over the state of the art on both tasks.
Problem
Sequential object-then-part segmentation can propagate errors from predicted object masks into part labels, although object context and part detail can benefit each other.
Source: §1 · p. 1Method
The approach groups similar parts across objects as semantic compositional parts and uses a two-channel FCN to predict SCP and object potentials for joint inference.
Source: §3.1 · p. 3, §3.2 · p. 3Results
Takeaways & Limitations
The joint formulation addresses subtle appearance differences between parts of one object and ambiguity across similar object categories.
Source: §1 · p. 1Automatic SCP learning and joint instance segmentation, which could improve object scale and address remaining localization issues, are left for future work.
Source: §5 · p. 8
Abstract
from arXiv · showhide
Segmenting semantic objects from images and parsing them into their respective semantic parts are fundamental steps towards detailed object understanding in computer vision. In this paper, we propose a joint solution that tackles semantic object and part segmentation simultaneously, in which higher object-level context is provided to guide part segmentation, and more detailed part-level localization is utilized to refine object segmentation. Specifically, we first introduce the concept of semantic compositional parts (SCP) in which similar semantic parts are grouped and shared among different objects. A two-channel fully convolutional network (FCN) is then trained to provide the SCP and object potentials at each pixel. At the same time, a compact set of segments can also be obtained from the SCP predictions of the network. Given the potentials and the generated segments, in order to explore long-range context, we finally construct an efficient fully connected conditional random field (FCRF) to jointly predict the final object and part labels. Extensive evaluation on three different datasets shows that our approach can mutually enhance the performance of object and part segmentation, and outperforms the current state-of-the-art on both tasks.
1. Introduction
Object segmentation has been widely studied, while part parsing has often focused on a few accurately localized categories, and sequential methods can propagate object-mask errors to parts. The paper proposes joint object and part segmentation in which object context guides part parsing and part detail refines object prediction.
1. Introduction
- Part parsing offers detailed object understanding, but prior semantic parsing largely addressed a few categories with accurate localization, such as humans and cars.Earlier joint work performed object segmentation first and then assigned part labels inside the predicted mask, allowing mask errors to propagate to parts.Source: §1 · p. 1
- Object segmentation needs broader context, whereas part segmentation emphasizes local detail and boundaries; jointly using both can address appearance differences among parts and ambiguity across similar object categories.The approach enforces object-part consistency through joint training and inference.Source: §1 · p. 1
- SCP groups similar parts across related object classes, allowing shared labels such as horse and cow legs to reduce part-label ambiguity.The object label resolves ambiguity among shared part labels.Source: §1 · pp. 1–2
- A two-channel FCN predicts semantic compositional part (SCP) and object potentials, while SCP-derived segments become nodes in an FCRF for joint label inference.The object potentials are refined using the SCP and object potentials together; long-range constraints enforce object-part consistency.Source: §1 · p. 2, Figure 2 · p. 2
- The joint approach improves both object and part segmentation and significantly outperforms the state of the art on both tasks.Experiments cover three datasets derived from the PASCAL VOC segmentation benchmark.Source: §1 · p. 2
2. Related work
Prior work explored semantic parts for recognition and parsing, but segmentation methods often assumed cropped, unoccluded objects. The paper positions its joint, part-sharing approach against sequential object-and-part segmentation and work using implicit part representations.
2. Related work
- Semantic part representations have supported detection and recognition, from implicit hidden parts in DPM to explicitly supervised parts and human-part Poselets.Animal-part annotations and richer part-segment labels are also described as supporting stronger detection and feature learning.Source: §2 · p. 2
- Earlier semantic parsing often used cropped, unoccluded humans, cars, or animals, limiting applicability to real-world occlusion, deformation, and appearance variation.The cited approaches include shape-based and appearance-based models.Source: §2 · p. 2
- CNNs revealed meaningful part-like representations, but these were not semantic parts; explicit semantic-part supervision for segmentation had not been investigated in the cited work.FCNs provide the paper's basis for explicitly modeling semantic parts alongside whole objects.Source: §2 · p. 2
- FCNs classify pixels within limited receptive fields and may miss long-range context, motivating object-scale context modeling to address local confusion.The text notes prior work on object-scale context.Source: §2 · p. 3
- Compared with hypercolumn segmentation, this approach shares part labels across classes and jointly models object and part potentials, reducing training complexity and improving scalability as object classes grow.The paper describes this as the first joint solution for semantic part and object segmentation.Source: §2 · p. 3
3. Joint part and object segmentation
The framework jointly predicts object and part labels through shared semantic compositional parts, FCN potentials, segment proposals, and a fully connected CRF. Its components connect object-scale context with detailed part localization.
3. Joint part and object segmentation
- The section develops shared SCPs, joint FCN potentials, SCP segment proposals, and an FCRF for jointly predicting object and part segmentation.Source: §3 · p. 3
3.1. Semantic compositional parts
- SCP labels share parts across related objects when semantic meaning, appearance, and shape are similar, reducing the label space while object labels preserve the specific part identity.Source: §3.1 · p. 3
3.2. Deep part and object potentials
- The two-channel FCN predicts SCP and object potentials, concatenates them to refine object potentials, and omits SCP refinement because it adds little and increases system complexity.The SCP and object streams use different input resolutions to obtain different receptive fields.Source: §3.2 · pp. 3–4
3.3. SCP segments proposal
- 85.1% best possible object IoU on quadrupeds under oracle labeling; SCP proposals group pixels assigned the same highest-probability SCP label.Source: §3.3 · p. 4
3.4. Joint FCRF
- FCRF groups nearby segments and connects every pair, using object-scale context to jointly infer object and SCP labels with object/SCP potentials and spatial or appearance cues.Source: §3.4 · pp. 4–5
3.5. Relation to the DPM structure
- The FCRF is analogous to DPM’s root-and-part structure, but learns pairwise spatial relationships through distances and relative angles rather than explicit root-part geometry rules.The FCN performs localization, and inference uses the smaller label space at object-scale context.Source: §3.5 · p. 5
4. Experiments
Experiments evaluate the approach on Horse-Cow, PASCAL Quadrupeds, and a PASCAL Part benchmark, with comparisons across these settings.
4.1. Model training.
- Training uses 16-stride FCNs, since 8-stride models improve by less than 1%; augmentation yields about 25 training samples per image.Source: §4.1 · p. 5
4.2. Parameters and details
- Larger input scales sharpen boundaries but increase local confusion; validation selected sp = 600 and so = 300.Source: Figure 5 · p. 6, §4.2 · p. 6
4.3. Performance comparisons
- Experiments compare SPS and HC for part segmentation and FCN for object segmentation using IOU; Horse-Cow assumes known object class, while Quadrupeds predicts both masks.Source: §4 · p. 5, §4.3 · pp. 6–7
- On Horse-Cow, the method averages 13% better than SPS and 4.5% better than HC.SPS struggles with deformable or occluded parts; HC can miss details such as legs because of object-mask inaccuracies.Source: §4.3 · p. 6, Table 1 · p. 7
- On Quadrupeds, the final model improves object segmentation 6.2% over FCN; joint potentials add another 4% over FCRF+FCN(16s).Source: Table 2 · p. 7, §4.3 · p. 7
- On Quadrupeds, mean IOU across parts is over 5% higher than HC, and the method captures boundaries sometimes missed by FCN and HC.Source: Table 2 · p. 7, §4.3 · p. 8
5. Conclusion and discussion
The authors report state-of-the-art results for their joint object-and-part segmentation framework, which complements pixel-wise Dense CRF methods. Strong appearance confusion and occlusion can mislead object scale or SCP segments, while automatic SCP learning and instance segmentation are future work.
5. Conclusion and discussion
- The joint framework is reported to yield state-of-the-art results and complements pixel-wise Dense CRF methods through part modeling and scale-adaptive handling of local ambiguity.The approach also tackles part segmentation beyond the object.Source: §5 · p. 8
- Strong appearance confusion and occlusion can mislead object scale or SCP segments, yielding inaccurate results.The authors give a dog's back leg as an example failure.Source: §5 · p. 8
- Future work includes automatically learning SCP and jointly modeling instance segmentation to improve object scale and address remaining localization issues.Source: §5 · p. 8
- Table 3 reports average precision on the part segmentation benchmark.Source: Table 3 · p. 8