Segmenting and recognizing a diverse range of object parts is crucial in various computer vision and robotic applications. While object segmentation has made significant progress, part-level segmentation remains an under-explored issue. Part segmentation entails discerning complex boundaries between parts, and the scarcity of annotated data further complicates the task. To tackle this problem, in this paper, we propose a novel Weakly-supervised Part Segmentation (WPS) setting and an approach called WPS-SAM, built on the large-scale pre-trained vision foundation model, Segment Anything Model (SAM). WPS-SAM is an end-to-end framework designed to extract prompt tokens directly from images and perform pixel-level segmentation of part regions. During its training phase, it only utilizes weakly supervised labels in the form of bounding boxes or points. Extensive experiments demonstrate that, through exploiting the rich knowledge embedded in pre-trained foundation models, WPS-SAM outperforms other segmentation models trained with pixel-level strong annotations. Specifically, WPS-SAM achieves 68.93% mIOU and 79.53% mACC on the PartImageNet dataset, surpassing state-of-the-art fully supervised methods by approximately 4% in terms of mIOU.

Text-to-image diffusion models have shown remarkable success in synthesizing photo-realistic images. Apart from creative applications, can we use such models to synthesize samples that aid the few-shot training of discriminative models? In this work, we propose AlignDiff, a general framework for synthesizing training images and masks for few-shot segmentation. We identify two crucial misalignments that arise when utilizing pre-trained diffusion models in segmentation tasks, which need to be addressed to create realistic training samples and align the synthetic data distribution with the real training distribution: 1) instance-level misalignment, where generated samples of rare categories are often misaligned with target tasks) and 2) annotation-level misalignment, where diffusion models are limited to generating images without pixel-level annotations. AlignDiff overcomes both challenges by leveraging a few real samples to guide the generation, thus improving novel IoU over baseline methods in few-shot segmentation and generalized few-shot segmentation on Pascal-5i and COCO-20i by up to 80%. Notably, AlignDiff is capable of augmenting the learning of out-of-distribution uncommon categories on FSS-1000, while naive diffusion model generates samples that diminish segmentation performance.

The Segment Anything Model (SAM) has demonstrated remarkable zero-shot capability and flexible geometric prompting in general image segmentation. However, it often struggles in domains that are either sparsely represented or lie outside its training distribution, such as aerial, medical, and non-RGB images. Recent efforts have predominantly focused on adapting SAM to these domains using fully supervised methods, which necessitate large amounts of annotated training data and pose practical challenges in data collection. This paper presents CAT-SAM, a ConditionAl Tuning network that explores few-shot adaptation of SAM toward various challenging downstream domains in a data-efficient manner. The core design is a prompt bridge structure that enables decoder-conditioned joint tuning of the heavyweight image encoder and the lightweight mask decoder. The bridging maps the domain-specific features of the mask decoder to the image encoder, fostering synergic adaptation of both components with mutual benefits with few-shot target samples only, ultimately leading to superior segmentation in various downstream tasks. We develop two CAT-SAM variants that adopt two tuning strategies for the image encoder: one injecting learnable prompt tokens in the input space and the other inserting lightweight adapter networks. Extensive experiments over 11 downstream tasks show that CAT-SAM achieves superior segmentation consistently even under the very challenging one-shot adaptation setup. Code will be available.

Pre-trained vision-language models, e.g., CLIP, have been increasingly used to address the challenging Open-Vocabulary Segmentation (OVS) task, benefiting from their well-aligned vision-text embedding space. Typical solutions involve either freezing CLIP during training to unilaterally maintain its zero-shot capability, or fine-tuning CLIP vision encoder to achieve perceptual sensitivity to local regions. However, few of them incorporate vision-text collaborative optimization. Based on this, we propose the Content-Dependent Transfer to adaptively enhance each text embedding by interacting with the input image, which presents a parameter-efficient way to optimize the text representation. Besides, we additionally introduce a Representation Compensation strategy, reviewing the original CLIP-V representation as compensation to maintain the zero-shot capability of CLIP. In this way, the vision and text representation of CLIP are optimized collaboratively, enhancing the alignment of the vision-text feature space. To the best of our knowledge, we are the first to establish the collaborative vision-text optimizing mechanism within the OVS field. Extensive experiments demonstrate our method achieves superior performance on popular OVS benchmarks. In open-vocabulary semantic segmentation, our method outperforms the previous state-of-the-art approaches by +0.5, +2.3, +3.4, +0.4 and +1.1 mIoU, respectively on A-847, A-150, PC-459, PC-59 and PAS-20. Furthermore, in a panoptic setting on the ADE20K dataset, we achieve the performance of 27.1 PQ, 73.5 SQ, and 32.9 RQ.

Unsupervised Domain Adaptation (UDA) for semantic segmentation has been widely studied to exploit the label-rich source data to assist the segmentation of unlabeled samples on target domain. Despite these efforts, UDA performance remains far below that of fully-supervised model owing to the lack of target annotations. To this end, we propose an efficient superpixel-level active learning method for domain adaptive semantic segmentation to maximize segmentation performance by automatically querying a small number of superpixels for labeling. To conserve annotation resources, we propose a novel low-uncertainty superpixel fusion module which amalgamates superpixels possessing low-uncertainty features based on feature affinity and thereby ensuring high-quality fusion of superpixels. As for the acquisition strategy, our method takes into account two types of information-rich superpixels: large-size superpixels with substantial information content, and superpixels with the greatest value for domain adaptation learning. Further, we employ the cross-domain mixing and pseudo label with consistency regularization techniques respectively to address the domain shift and label noise problems. Extensive experimentation demonstrates that our proposed superpixel-level method utilizes a limited budget more efficiently than previous pixel-level techniques and surpasses state-of-the-art methods at 40x lower cost.

Delving into the realm of egocentric vision, the advancement of referring video object segmentation (RVOS) stands as pivotal in understanding human activities. However, existing RVOS task primarily relies on static attributes such as object names to segment target objects, posing challenges in distinguishing target objects from background objects and in identifying objects undergoing state changes. To address these problems, this work proposes a novel action-aware RVOS setting called ActionVOS, aiming at segmenting only active objects in egocentric videos using human actions as a key language prompt. This is because human actions precisely describe the behavior of humans, thereby helping to identify the objects truly involved in the interaction and to understand possible state changes. We also build a method tailored to work under this specific setting. Specifically, we develop an action-aware labeling module with an efficient action-guided focal loss. Such designs enable ActionVOS model to prioritize active objects with existing readily-available annotations. Experimental results on VISOR dataset reveal that ActionVOS significantly reduces the mis-segmentation of inactive objects, confirming that actions help the ActionVOS model understand objects' involvement. Further evaluations on VOST and VSCOS datasets show that the novel ActionVOS setting enhances segmentation performance when encountering challenging circumstances involving object state changes.

Image modality is not perfect as it often fails in certain conditions, e.g., night and fast motion. This significantly limits the robustness and versatility of existing multi-modal (i.e., Image+X) semantic segmentation methods when confronting modality absence or failure, as often occurred in real-world applications. Inspired by the open-world learning capability of multi-modal vision-language models (MVLMs), we explore a new direction in learning the modality-agnostic representation via knowledge distillation (KD) from MVLMs. Intuitively, we propose Any2Seg, a novel framework that can achieve robust segmentation from any combination of modalities in any visual conditions. Specifically, we first introduce a novel language-guided semantic correlation distillation (LSCD) module to transfer both inter-modal and intra-modal semantic knowledge in the embedding space from MVLMs, e.g., LanguageBind. This enables us to minimize the modality gap and alleviate semantic ambiguity to combine any modalities in any visual conditions. Then, we introduce a modality-agnostic feature fusion (MFF) module that reweights the multi-modal features based on the inter-modal correlation and selects the fine-grained feature. This way, our Any2Seg finally yields an optimal modality-agnostic representation. Extensive experiments on two benchmarks with four modalities demonstrate that Any2Seg achieves the state-of-the-art under the multi-modal setting (+3.54 mIoU) and excels in the challenging modality-incomplete setting (+19.79 mIoU).

Open-vocabulary segmentation is the task of segmenting anything that can be named in an image. Recently, large-scale vision-language modelling has led to significant advances in open-vocabulary segmentation, but at the cost of gargantuan and increasing training and annotation efforts. Hence, we ask if it is possible to use existing foundation models to synthesise on-demand efficient segmentation algorithms for specific class sets, making them applicable in an open-vocabulary setting without the need to collect further data, annotations or perform training. To that end, we present OVDiff, a novel method that leverages generative text-to-image diffusion models for unsupervised open-vocabulary segmentation. OVDiff synthesises support image sets for arbitrary textual categories, creating for each a set of prototypes representative of both the category and its surrounding context (background). It relies solely on pre-trained components and outputs the synthesised segmenter directly, without training. Our approach shows strong performance on a range of benchmarks, obtaining a lead of more than 5% over prior work on PASCAL VOC.