Tackling Co-occurrence in Weakly Supervised Semantic Segmentation

Khái niệm cốt lõi

The author proposes a 'Separate and Conquer' scheme to address the co-occurrence issue in weakly supervised semantic segmentation by decomposing images and enhancing contrastive representation.
The main thesis of the author is that by separating co-occurring objects and enhancing category-specific representation, the co-occurrence problem can be effectively tackled without external supervision.

Tóm tắt

In this content, the authors introduce a novel approach called 'Separate and Conquer' (SeCo) to address the challenging co-occurrence problem in weakly supervised semantic segmentation. The method involves image decomposition to separate co-occurring objects and enhance semantic representation through multi-granularity knowledge contrast. By utilizing a dual-teacher-single-student architecture, they streamline the WSSS pipeline end-to-end, demonstrating superior performance over existing methods on PASCAL VOC and MS COCO datasets.
The content discusses the limitations of existing methods in handling co-occurring objects due to confusion caused by coupled contexts. It introduces SeCo as an efficient solution that decouples co-occurring objects through image decomposition and enhances semantic representation with contrastive learning. Extensive experiments validate the effectiveness of SeCo in addressing the co-occurrence issue without external supervision.
The proposed method outperforms both single-staged and multi-staged competitors on PASCAL VOC and MS COCO datasets, showcasing its efficiency in tackling co-occurrence problems. Through detailed analysis and comparisons with state-of-the-art methods, the authors demonstrate how SeCo significantly improves segmentation results by accurately localizing co-occurring objects.

Thống kê

Extensive experiments are conducted on PASCAL VOC and MS COCO datasets.
SeCo achieves 74.0% mIoU on PASCAL VOC test set.
SeCo surpasses other competitors with 0.23 confusion ratio for all categories on VOC val set.
SeCo takes 417 minutes to finish training workflow efficiently.

Trích dẫn

"The key insight lies in the 'separate and conquer' training scheme that decouples co-occurrence in image space and feature space."
"Our method demonstrates strength at accurately activating the co-contexts."

Thông tin chi tiết chính được chắt lọc từ

Separate and Conquer

by Zhiwei Yang,... lúc arxiv.org 02-29-2024

https://arxiv.org/pdf/2402.18467.pdf

Yêu cầu sâu hơn

How can SeCo's approach be adapted for other computer vision tasks beyond semantic segmentation

SeCo's approach can be adapted for other computer vision tasks by modifying the image decomposition and representation enhancement techniques to suit the specific requirements of each task. For instance, in object detection, the image could be decomposed into regions of interest instead of patches, and the representation enhancement could focus on extracting features relevant to detecting objects rather than segmenting them. Similarly, for image classification tasks, the decomposition could involve dividing images into smaller sections based on key visual elements, while the representation enhancement could emphasize learning discriminative features for classification.

What potential challenges or biases could arise from using image-level labels for weakly supervised tasks like semantic segmentation

Using image-level labels for weakly supervised tasks like semantic segmentation can introduce challenges and biases due to limited supervision. One potential challenge is ambiguity in labeling multiple objects within an image that may lead to incorrect segmentation boundaries or false positives. Additionally, co-occurring objects may share similar visual characteristics that make it challenging for models to differentiate between them accurately. Biases may arise from relying solely on high-level annotations without detailed pixel-level information, leading to oversimplified representations or misinterpretations of complex scenes.

How might advancements in contrastive learning techniques further enhance SeCo's performance in addressing complex relations among categories

Advancements in contrastive learning techniques can further enhance SeCo's performance by improving feature discrimination and capturing fine-grained relationships among categories. By incorporating more sophisticated contrastive loss functions that consider not only similarities but also differences between samples at different levels (e.g., patch level vs. global level), SeCo can better disentangle co-occurring contexts and reduce noise during feature representation. Techniques like multi-granularity contrast and adaptive updating strategies for category knowledge extraction can help address complex relations among categories more effectively by providing a richer understanding of semantic context and enhancing model generalization capabilities.