Recent progress in semi-supervised video object segmentation has largely hinged on memory-based methods. However, when faced with increasingly tough challenges emerging in complex scenarios, such as fundamental semantic transformations and severe spatial deformations, the fixed-interval memory update mechanism usually adopted in these memory-based methods is insufficient to align with the pivotal moments of object changes. This inflexible mechanism motivates us to design an adaptive memory update mechanism in response to the semantic-spatial changes of target objects. To this end, we propose a novel Change-Sensitive Network (CSNet) to learn when and how to update memory to effectively address intricate challenges in complex scenarios. Specifically, we first design an Adaptive Perception-Capture module with a hierarchical contrastive learning loss to determine when to update memory moments by measuring the extent of object changes, thus dividing entire videos into different object-change clips. To further extract and highlight object changes to assist in the segmentation of frames after changes occur, we construct Dynamic Memory Update modules to redefine how to update memory by smoothly retaining the object prototypes within clips and dynamically amplifying the object variations across clips. Extensive experiments demonstrate that our proposed CSNet exhibits clear superiority when evaluated on eight datasets covering three kinds: common, complex and long-video datasets.

Recently, memory-based methods have achieved progress in semi-supervised video object segmentation. However, these methods still suffer from unstructured challenges, such as object transformations, occlusions and disappearance-reappearance. To this end, we propose a Holistic Correction Network (HCNet) to adaptively acquire concise object prototypes for holistic correction at semantic, spatial and temporal aspects. Specifically, an Adaptive Prototype Update module is firstly designed to construct multi-level core object representations by associating object variations in consecutive frames with segmentation quality assessment. Based on the updated object prototypes, Semantic, Spatial and Temporal Correction modules are respectively designed to enhance the object semantics in the entire frame, eliminate the incorrect semantic enhancement outside the object regions and calibrate the estimated object regions with temporal changes of objects. Through the holistic correction mechanism with effective object prototypes, our proposed HCNet can robustly and efficiently deal with diverse complex scenarios. Extensive and comprehensive experiments conducted on seven datasets demonstrate that our proposed HCNet can significantly improve the segmentation performance.

Camouflaged instance segmentation (CIS) aims to detect and segment objects blending with their surroundings. While existing CIS methods rely heavily on fully-supervised training with massive precisely annotated data, consuming considerable annotation efforts yet struggling to segment highly camouflaged objects accurately. Despite their visual similarity to the background, camouflaged objects differ semantically. Since text associated with images offers explicit semantic cues to underscore this difference, we propose a novel approach: the first Text-Prompt based weakly-supervised camouflaged instance segmentation method named TPNet, leveraging semantic distinctions for effective segmentation. TPNet operates in two stages: pseudo mask generation and a self-training process. In the first stage, we align text prompts with images using a language-image model to obtain region proposals containing camouflaged instances. A Semantic-Spatial Iterative Fusion module is designed to assimilate spatial information with semantic insights, iteratively refining pseudo mask. In the second stage, Graduated Camouflage Learning, a self-training strategy, sequences training from simple to complex images based on camouflage levels, facilitating an effective learning gradient. Through the collaboration of the dual phases, our method offers a comprehensive experiment on two common benchmark and demonstrates a significant advancement, delivering a novel solution that bridges the gap between weak-supervised and high camouflaged instance segmentation.

Existing salient object detection methods often adopt deeper and wider networks for better performance, resulting in heavy computational burden and slow inference speed. This inspires us to rethink saliency detection to achieve a favorable balance between efficiency and accuracy. To this end, we design a lightweight framework while maintaining satisfying competitive accuracy. Specifically, we propose a novel trilateral decoder framework by decoupling the U-shape structure into three complementary branches, which are devised to confront the dilution of semantic context, loss of spatial structure and absence of boundary detail, respectively. Along with the fusion of three branches, the coarse segmentation results are gradually refined in structure details and boundary quality. Without adding additional learnable parameters, we further propose Scale-Adaptive Pooling Module to obtain multi-scale receptive field. In particular, on the premise of inheriting this framework, we rethink the relationship among accuracy, parameters and speed via network depth-width tradeoff. With these insightful considerations, we comprehensively design shallower and narrower models to explore the maximum potential of lightweight SOD. Our models are proposed for different application environments: 1) a tiny version CTD-S (1.7M, 125FPS) for resource constrained devices, 2) a fast version CTD-M (12.6M, 158FPS) for speed-demanding scenarios, 3) a standard version CTD-L (26.5M, 84FPS) for high-performance platforms. Extensive experiments validate the superiority of our method, which achieves better efficiency-accuracy balance across five benchmarks.