The conditional diffusion models have made significant progress in image synthesis, leveraging human annotations such as class labels or text descriptions to guide the generative process. However, different from image synthesis, low-light image enhancement(LLIE) lacks strictly calibrated conditional priors to guide the enhancement process, often resulting in unsatisfactory results. To address the issue, we propose Retinex-Based Self-Conditioned Diffusion Models, dubbed RSCDM, which utilizes self-conditioned illumination representation learning and representation guidance enhancement to generate high-quality image. To be specific, in the first stage, we pretrain a retinex decomposed model (RDM) to capture illumination representation and devise a illumination-representation restoration model (IRM) to accurately reconstruct the representation from noisy images. Moreover, we further design dynamic resblock (DRB) and dynamic simplified attention gated block (DSAGB) as basic units of IRM for better fine-grained restoration. In the second stage, we employ a self-conditioned diffusion model (SDM) to generate realistic results conditioned on the illumination representation. Extensive experiments demonstrates our method outperforms the existing SOTA methods both quantitatively and qualitatively. The codes will be publicly available.

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.

Unforeseen appearance variation is a challenging factor for visual tracking. This paper provides a novel solution from semantic data augmentation, which facilitates offline training of trackers for better generalization. We utilize existing samples to obtain knowledge to augment another in terms of diversity and hardness. First, we propose that the similarity matching space in Siamese-like models has class-agnostic transferability. Based on this, we design the Latent Augmentation (LaAug) to transfer relevant variations and suppress irrelevant ones between training similarity embeddings of different classes. Thus the model can generalize across a more diverse semantic distribution. Then, we propose the Semantic Interaction Mix (SIMix), which interacts moments between different feature samples to contaminate structure and texture attributes and retain other semantic attributes. SIMix simulates the occlusion and complements the training distribution with hard cases. The mixed features with adversarial perturbations can empirically enable the model against external environmental disturbances. Experiments on six challenging benchmarks demonstrate that three representative tracking models, i.e., SiamBAN, TransT and OSTrack, can be consistently improved by incorporating the proposed methods without extra parameters and inference cost.

Most of the previous exposure correction methods learn dense pixel-wise transformations to achieve promising results, but consume huge computational resources. Recently, Learnable 3D lookup tables (3D LUTs) have demon-strated impressive performance and efficiency for image enhancement. However, these methods can only perform global transformations and fail to finely manipulate local regions. Moreover, they uniformly downsample the input image, which loses the rich color information and limits the learning of color transformation capabilities. In this paper, we present a collaborative transformation framework (CoTF) for real-time exposure correction, which integrates global transformation with pixel-wise transformations in an efficient manner. Specifically, the global transformation adjusts the overall appearance using image-adaptive 3D LUTs to provide decent global contrast and sharp details, while the pixel transformation compensates for local context. Then, a relation-aware modulation module is designed to combine these two components effectively. In addition, we propose an adaptive sampling strategy to preserve more color information by predicting the sampling intervals, thus providing higher quality input data for the learning of 3D LUTs. Extensive experiments demonstrate that our method can process high-resolution images in real-time on GPUs while achieving comparable performance against current state-of-the-art methods. The code is avail-able at https://github.com/HUST-IAL/CoTF.

Recently, deep learning-based methods have been successfully applied to the field of exposure correction. However, most of the existing methods treat different locations of an image in the same way, ignoring the inhomogeneous recovery difficulty and spatially-varying visual patterns in the image, which is sub-optimal and not perfectly efficient. In this paper, we propose a difficulty-aware dynamic network (DDNet) for lightweight exposure correction. Specifically, we propose a difficulty-aware strategy that determines the difficulty of feature patches according to a difficulty mask. Then, only the difficult patches are further refined instead of the whole features, which greatly reduces the overall computational complexity. Moreover, in order to achieve spatially-varying processing with a minimal computational burden, we design a spatial-aware dynamic convolution (SDConv), which is generated by predicting a set of basic kernels and a spatial-aware weight map. Benefiting from these designs, our method can strike a good trade-off between performance and complexity. Extensive experiments on several datasets demonstrate that our approach outperforms the state-of-the-art methods both qualitatively and quantitatively while requiring cheaper computational costs.

Nighttime tracking is challenging due to the lack of sufficient training data and scene diversity. Unsupervised domain adaptation is a solution by transferring knowledge from day (source domain) to night (target domain). It typically involves adversarial training with a domain discriminator on the source and target data to learn domain-invariant features. However, the imbalanced source/target distribution can cause overfitting of the domain discriminator, hindering the domain adaptability. To address this issue, we propose a Progressive Domain-Style Translation (PDST) for domain adaptive nighttime tracking. PDST decomposes and recombines domain-invariant content encodings and domain-specific style encodings of different domains. Thus the rich source domain content is translated to the target domain, expanding the inter-class diversity of the target domain to alleviate overfitting. Moreover, a momentum update manner is introduced to progressively estimate the domain-style encoding from multiple features, which more accurately reflects the statistical domain attribute than an individual image-style. Finally, we incorporate two regularization terms to constrain the content and domain-style consistency in the translation process, ensuring the generated source-like target features are valid to facilitate the training of domain adaptation. Exhaustive experiments demonstrate the domain adaptability and SOTA performance of the proposed method in nighttime tracking.