Our paper introduces a novel two-stage self-supervised approach for detecting
co-occurring salient objects (CoSOD) in image groups without requiring
segmentation annotations. Unlike existing unsupervised methods that rely solely
on patch-level information (e.g. clustering patch descriptors) or on
computation heavy off-the-shelf components for CoSOD, our lightweight model
leverages feature correspondences at both patch and region levels,
significantly improving prediction performance. In the first stage, we train a
self-supervised network that detects co-salient regions by computing local
patch-level feature correspondences across images. We obtain the segmentation
predictions using confidence-based adaptive thresholding. In the next stage, we
refine these intermediate segmentations by eliminating the detected regions
(within each image) whose averaged feature representations are dissimilar to
the foreground feature representation averaged across all the cross-attention
maps (from the previous stage). Extensive experiments on three CoSOD benchmark
datasets show that our self-supervised model outperforms the corresponding
state-of-the-art models by a huge margin (e.g. on the CoCA dataset, our model
has a 13.7% F-measure gain over the SOTA unsupervised CoSOD model). Notably,
our self-supervised model also outperforms several recent fully supervised
CoSOD models on the three test datasets (e.g., on the CoCA dataset, our model
has a 4.6% F-measure gain over a recent supervised CoSOD model).

我们的论文介绍了一种新颖的两阶段自监督方法，用于在图像组中检测共现显著对象（CoSOD），不需要分割注释。我们的轻量级模型利用了补丁和区域级别上的特征对应，显著提高了预测性能。我们的自监督模型在三个 CoSOD 基准数据集上进行了大量实验证明，性能明显优于现有最先进模型。

多尺度特征对应下的自监督共显目标检测

Self-supervised co-salient object detection via feature correspondence  at multiple scales

Few-shot semantic segmentation (FSS) aims to segment objects of unseen
classes in query images with only a few annotated support images. Existing FSS
algorithms typically focus on mining category representations from the
single-view support to match semantic objects of the single-view query.
However, the limited annotated samples render the single-view matching struggle
to perceive the reflection invariance of novel objects, which results in a
restricted learning space for novel categories and further induces a biased
segmentation with demoted parsing performance. To address this challenge, this
paper proposes a fresh few-shot segmentation framework to mine the reflection
invariance in a multi-view matching manner. Specifically, original and
reflection support features from different perspectives with the same semantics
are learnable fused to obtain the reflection invariance prototype with a
stronger category representation ability. Simultaneously, aiming at providing
better prior guidance, the Reflection Invariance Prior Mask Generation (RIPMG)
module is proposed to integrate prior knowledge from different perspectives.
Finally, segmentation predictions from varying views are complementarily merged
in the Reflection Invariance Semantic Prediction (RISP) module to yield precise
segmentation predictions. Extensive experiments on both PASCAL-$5^\textit{i}$
and COCO-$20^\textit{i}$ datasets demonstrate the effectiveness of our approach
and show that our method could achieve state-of-the-art performance. Code is
available at https://anonymous.4open.science/r/RILFS-A4D1

本文提出了一个新的少样本分割框架，通过多视角匹配的方式挖掘反射不变性。通过学习融合具有相同语义的原始和反射支持特征，得到具有更强分类表示能力的反射不变性原型。同时，还提出了反射不变性先验掩码生成模块以集成不同视角的先验知识，并通过反射不变性语义预测模块将来自不同视角的分割预测进行综合，从而得出准确的分割预测结果。在 PASCAL-5i 和 COCO-20i 数据集上的广泛实验证明了我们方法的有效性和卓越性能。