Video Camouflaged Object Detection (VCOD) poses significant challenges due to the subtle appearance of camouflaged objects, especially under dynamic motion and occlusion. Existing methods predominantly rely on optical flow or black-box features for motion modeling, which often entail substantial computational costs and suffer from limited interpretability. Inspired by the human strategy of identifying abnormal movements between frames and the principle of event camera image formation, we propose an eventstream-inspired dual-branch framework for VCOD. Specifically, we design an eventstream-like data extraction module to capture pixel-level motion variations, effectively distinguishing object motion from background dynamics. This event-based representation is integrated into SAM2 through a dual-branch memory-augmented framework, consisting of Time Bridge Attention and Visual Bridge Attention, enabling joint modeling of motion and appearance cues. In addition, we introduce a Prompt Embedding Generator to eliminate the need for human-provided interactive prompts, facilitating fully automatic VCOD. Extensive experiments on MoCA-Mask and CAD2016 demonstrate that our approach significantly outperforms state-of-the-art methods, achieving both superior segmentation accuracy and interpretable motion modeling. To the best of our knowledge, this is the first work to incorporate eventstream-inspired representations into the VCOD task.

Current vision-language trackers often struggle to fuse multimodal information comprehensively and effectively, leading to suboptimal performance in multimodal tasks. This study introduces LGTrack, a novel language-guided visual tracking framework designed to achieve a more comprehensive and efficient fusion of vision and language information. In the encoding stage, an Enhanced Multimodal Interaction Module is proposed to achieve full multimodal fusion, and it is used to construct Early Language Multilevel-guided Multimodal Encoding, which leverages deep semantic information for early and multilevel guidance of vision encoding. In the decoding stage, a multimodal decoding based on Joint Query is proposed, utilizing global features from both vision and language modalities, guiding the efficient operation of the decoding layers. These innovations achieve a more comprehensive fusion of multimodal information. Additionally, a contrastive learning strategy is introduced to align vision-language features in the semantic space, further enhancing the fusion effectiveness. Extensive experiments on multiple benchmarks such as LaSOT, \(\rm{LaSOT_{ext}}\) , TNL2K, and OTB99-Lang demonstrate that our approach outperforms existing state-of-the-art trackers.