Changxin Gao

Video dehazing aims to restore clean scenarios from a sequence of hazy frames, where frame alignment is a critical stage for leveraging temporal information. However, haze degrades contrast and obscures details, making alignment challenging. Existing methods ignore the impairment of haze on alignment and thus struggle to align frames accurately. To address this challenge, we propose an alignment network with the temporal lookup table (temporal-LUT), which effectively enhances the haze-degraded frames and provides vivid cues for precise alignment. Specifically, to tackle the color degradation of haze, we employ a learnable lookup table (LUT) to enhance hazy color. The color mapping nature of LUT favorably preserves the naturalness of enhanced outcomes. Besides, we introduce a temporal weight prediction strategy to strengthen inter-frame interaction, which ensures temporal consistency across enhanced results and thereby benefits alignment. Extensive experimental results on two widely used benchmarks and real-world scenes demonstrate the superiority of our method.

Weakly Supervised Incremental Learning for Semantic Segmentation (WILSS) aims to segment novel classes while avoiding catastrophic forgetting based on image-level labels. However, WILSS faces two challenges. First, since features of all classes share a finite latent space, excessively dispersed base-class features limit the capacity for novel classes. Second, the limited information provided by image-level labels inevitably leads to low-quality pseudo labels, which not only hinders the learning of novel classes but also exacerbates catastrophic forgetting. To address these issues, we propose two strategies: Feature Distribution Regularization (FDR) and Pseudo Label Refinement (PLR), which are applied on the base and incremental steps, respectively. FDR employs Gaussian Mixture Models (GMMs) to model the feature distribution of each base class, and enforces each feature to approach the center of its corresponding class's GMMs, thereby making the feature distribution of base classes more compact and reserving sufficient space for novel classes. PLR improves pseudo labels from two perspectives. First, it proposes a Prototype Classification Loss (Lpcl )to ensure each prototype is correctly assigned to its corresponding class, which is beneficial for enlarging inter-class differences and helps mitigate the inter-class confusion in pseudo labels. Second, it leverages the predictions of EMA model to adaptively refurbish the pseudo labels, as the EMA model is inherently robust to noise. The effective integration of these two methods not only facilitates the learning of novel classes but also helps mitigate catastrophic forgetting. Extensive experiments conducted on PASCAL and COCO datasets validate the effectiveness of our method.

Understanding the structure of characters is crucial for recovering clear and readable high-resolution scene text images in Scene Text Image Super-Resolution (STISR). Recently, many existing STISR methods inject the character structure information implicit in the recognition priors into the super-resolution network to guide the super-resolution process, thereby facilitating the generation of more legible text images. However, the recognition priors obtained from low resolution are inaccurate, which means that directly embedding these priors into the network easily misleads the super-resolution process. To address this problem, we draw inspiration from Masked Image Modeling (MIM) and propose the Mask Structure Inference Network (MSINet), which can generate scene text images with accurate character structures without directly embedding recognition priors. To make STISR compatible with MIM, we also propose a Mask-and-Inference Paradigm (MIP), which consists of a mask image pre-training stage for character structure learning and a fine-tuning stage for character structure inference. In addition, a novel mask strategy named Text Confidence Mask (TCM) is proposed to avoid recovery errors by masking legible character regions. With MIP and TCM, MSINet impressively improves the clarity and readability of the degraded scene text images. Specifically, MSINet-B outperforms recent state-of-the-art methods by about +3.7% on the TextZoom and average +3.6% on six manually degraded scene text recognition datasets in recognition accuracy. The code will be released at https://github.com/Yuanssr/MSINet.