Learning from a large corpus of data, pre-trained models have achieved impressive progress nowadays. As a popular generative pre-training method, diffusion models stand out by capturing both low-level visual knowledge and high-level semantic relations. In this paper, we propose to exploit such knowledgeable pre-trained diffusion models for mainstream discriminative tasks such as annotation-free saliency segmentation. However, a notable structural discrepancy between generative and discriminative models poses a significant challenge to diffusion models’ direct application. Furthermore, the absence of explicit manually labeled data is a substantial barrier in annotation-free settings. To tackle these issues, we introduce FreeSegDiff, one novel synthesis-exploitation framework containing two-stage strategies. In the first synthesis stage, to alleviate data insufficiency, we synthesize abundant images, and propose a novel training-free DiffusionCut to produce masks. In the second exploitation stage, to bridge the structural gap, we employ the inversion technique to convert given images back to diffusion features. These features seamlessly integrate with downstream architectures. Extensive experiments and ablation studies demonstrate the superiority of adapting diffusion for annotation-free saliency segmentation.

Video understanding is a complex challenge that requires effective modeling of spatial-temporal dynamics. With the success of image foundation models (IFMs) in image understanding, recent approaches have explored parameter-efficient fine-tuning (PEFT) to adapt IFMs for video. However, most of these methods tend to process spatial and temporal information separately, which may fail to capture the full intricacy of video dynamics. In this paper, we propose MoMa, an efficient adapter framework that achieves full spatial-temporal modeling by integrating Mamba's selective state space modeling into IFMs. We propose a novel SeqMod operation to inject spatial-temporal information into pre-trained IFMs, without disrupting their original features. By incorporating SeqMod into a Divide-and-Modulate architecture, MoMa enhances video understanding while maintaining computational efficiency. Extensive experiments on multiple video benchmarks demonstrate the effectiveness of MoMa, achieving superior performance with reduced computational cost. Codes will be released upon publication.

Referring Image Segmentation (RIS) aims to segment the target object in an image given a natural language expression. While recent methods leverage pre-trained vision backbones and more training corpus to achieve impressive results, they predominantly focus on simple expressions—short, clear noun phrases like “red car” or “left girl”. This simplification often reduces RIS to a key word/concept matching problem, limiting the model’s ability to handle referential ambiguity in expressions. In this work, we identify two challenging real-world scenarios: object-distracting expressions, which involve multiple entities with contextual cues, and category-implicit expressions, where the object class is not explicitly stated. To address the challenges, we propose a novel framework, SaFiRe, which mimics the human two-phase cognitive process—first forming a global understanding, then refining it through detail-oriented inspection. This is naturally supported by Mamba’s scan-then-update property, which aligns with our phased design and enables efficient multi-cycle refinement with linear complexity. We further introduce aRefCOCO, a new benchmark designed to evaluate RIS models under ambiguous referring expressions. Extensive experiments on both standard and proposed datasets demonstrate the superiority of SaFiRe over state-of-the-art baselines. Project page: https://zhenjiemao.github.io/SaFiRe/.