Image reflection separation aims to disentangle the transmission layer and the reflection layer from a blended image. Existing methods rely on limited information from a single image, tending to confuse the two layers when their contrasts are similar, a challenge more severe at night. To address this issue, we propose the Depth-Memory Decoupling Network (DMDNet). It employs the Depth-Aware Scanning (DAScan) to guide Mamba toward salient structures, promoting information flow along semantic coherence to construct stable states. Working in synergy with DAScan, the Depth-Synergized State-Space Model (DS-SSM) modulates the sensitivity of state activations by depth, suppressing the spread of ambiguous features that interfere with layer disentanglement. Furthermore, we introduce the Memory Expert Compensation Module (MECM), leveraging cross-image historical knowledge to guide experts in providing layer-specific compensation. To address the lack of datasets for nighttime reflection separation, we construct the Nighttime Image Reflection Separation (NightIRS) dataset. Extensive experiments demonstrate that DMDNet outperforms state-of-the-art methods in both daytime and nighttime.

Existing image reflection removal methods struggle to handle complex reflections. Accurate language descriptions can help the model understand the image content to remove complex reflections. However, due to blurred and distorted interferences in reflected images, machine-generated language descriptions of the image content are often inaccurate, which harms the performance of language-guided reflection removal. To address this, we propose the Adaptive Language-Aware Network (ALANet) to remove reflections even with inaccurate language inputs. Specifically, ALANet integrates both filtering and optimization strategies. The filtering strategy reduces the negative effects of language while preserving its benefits, whereas the optimization strategy enhances the alignment between language and visual features. ALANet also utilizes language cues to decouple specific layer content from feature maps, improving its ability to handle complex reflections. To evaluate the model's performance under complex reflections and varying levels of language accuracy, we introduce the Complex Reflection and Language Accuracy Variance (CRLAV) dataset. Experimental results demonstrate that ALANet surpasses state-of-the-art methods for image reflection removal. The code and dataset are available at https://github.com/fashyon/ALANet.

End-to-end Multiple Object Tracking (MOT) frameworks integrate detection and tracking into a unified model, avoiding intermediate information loss and complicated post-processing. However, existing end-to-end MOT trackers rely on track queries of the previous frame to provide prior information. Their limited short-term temporal modeling struggle to cope with high dynamic tracking scenarios, where inter-frame target variations exhibit significant heterogeneity. To address these shortcomings, we propose a scene-perception MOT framework (SP-MOT) that encodes scene context understanding into long-term embedding and adaptively complements it with short-term cues, enabling discriminative and flexible instance representations. Specifically, SP-MOT introduces: (1) a learnable scene query that globally profiles foreground and background to capture short-term scene-level features; (2) a context understanding module to uncover long-term stable relationships across dynamic scenes based on multiple historical scene features; (3) scene-adaptive augmented decoding that leverages scene information as guidance, adaptively aggregating long-term and short-term information into object embeddings, improving the model's association ability and fault-tolerance. Extensive experiments on MOT benchmarks demonstrate that SP-MOT outperforms state-of-the-art end-to-end trackers across multiple metrics, particularly in challenging scenarios with high dynamics.