Long video generation involves generating extended videos using models trained on short videos, suffering from distribution shifts due to varying frame counts. It necessitates the use of local information from the original short frames to enhance visual and motion quality, and global information from the entire long frames to ensure appearance consistency. Existing training-free methods struggle to effectively integrate the benefits of both, as appearance and motion in videos are closely coupled, leading to motion inconsistency and visual quality. In this paper, we reveal that global and local information can be precisely decoupled into consistent appearance and motion intensity information by applying Principal Component Analysis (PCA), allowing for refined complementary integration of global consistency and local quality. With this insight, we propose FreePCA, a training-free long video generation paradigm based on PCA that simultaneously achieves high consistency and quality. Concretely, we decouple consistent appearance and motion intensity features by measuring cosine similarity in the principal component space. Critically, we progressively integrate these features to preserve original quality and ensure smooth transitions, while further enhancing consistency by reusing the mean statistics of the initial noise. Experiments demonstrate that FreePCA can be applied to various video diffusion models without requiring training, leading to substantial improvements. Code is available at https://github.com/JosephTiTan/FreePCA.

In this work, we first revisit the sampling issues in current autoregressive (AR) image generation models and identify that image tokens, unlike text tokens, exhibit lower information density and non-uniform spatial distribution. Accordingly, we present an entropy-informed decoding strategy that facilitates higher autoregressive generation quality with faster synthesis speed. Specifically, the proposed method introduces two main innovations: 1) dynamic temperature control guided by spatial entropy of token distributions, enhancing the balance between content diversity, alignment accuracy, and structural coherence in both mask-based and scale-wise models, without extra computational overhead, and 2) entropy-aware acceptance rules in speculative decoding, achieving near-lossless generation at about 85% of the inference cost of conventional acceleration methods. Extensive experiments across multiple benchmarks using diverse AR image generation models demonstrate the effectiveness and generalizability of our approach in enhancing both generation quality and sampling speed.

Masked-based autoregressive models have demonstrated promising image generation capability in continuous space. However, their potential for video generation remains under-explored. Masked-based autoregressive models have demonstrated promising image generation capability in continuous space. However, their potential for video generation remains under-explored. In this paper, we propose \textbf{VideoMAR}, a concise and efficient decoder-only autoregressive image-to-video model with continuous tokens, composing temporal frame-by-frame and spatial masked generation. We first identify temporal causality and spatial bi-directionality as the first principle of video AR models, and propose the next-frame diffusion loss for the integration of mask and video generation. Besides, the huge cost and difficulty of long sequence autoregressive modeling is a basic but crucial issue. To this end, we propose the temporal short-to-long curriculum learning and spatial progressive resolution training, and employ progressive temperature strategy at inference time to mitigate the accumulation error. Furthermore, VideoMAR replicates several unique capacities of language models to video generation. It inherently bears high efficiency due to simultaneous temporal-wise KV cache and spatial-wise parallel generation, and presents the capacity of spatial and temporal extrapolation via 3D rotary embeddings. On the VBench-I2V benchmark, VideoMAR surpasses the previous state-of-the-art (Cosmos I2V) while requiring significantly fewer parameters ($9.3\%$), training data ($0.5\%$), and GPU resources ($0.2\%$).

Images corrupted by rain streaks often lose vital frequency information for perception, and image deraining aims to solve this problem, which relies on global and local degradation modeling. Recent studies have witnessed the effectiveness and efficiency of Mamba for perceiving global and local information based on its exploiting local correlation among patches, however, rarely attempts have been explored to extend it with frequency analysis for image deraining, limiting its ability to perceive global degradation that is relevant to frequency modeling (e.g. Fourier transform). In this paper, we propose FreqMamba, an effective and efficient paradigm that leverages the complementary between Mamba and frequency analysis for image deraining. The core of our method lies in extending Mamba with frequency analysis from two perspectives: extending it with frequency band for exploiting frequency correlation, and connecting it with Fourier transform for global degradation modeling. Specifically, FreqMamba introduces complementary triple interaction structures including spatial Mamba, frequency-band Mamba, and Fourier global modeling. Frequency-Band Mamba decomposes the image into sub-bands of different frequencies to allow 2D scanning from the frequency dimension. Furthermore, leveraging Mamba's unique data-dependent properties, we use rainy images at different scales to provide degradation priors to the network, thereby facilitating efficient training. Extensive experiments show that our method outperforms state-of-the-art methods both visually and quantitatively. Our code is available at: https://github.com/aSleepyTree/FreqMamba.

Deep image recognition models suffer a significant performance drop when applied to low-quality images since they are trained on high-quality images. Although many studies have investigated to solve the issue through image restoration or domain adaptation, the former focuses on visual quality rather than recognition quality, while the latter requires semantic annotations for task-specific training. In this paper, to address more practical scenarios, we propose a Visual Recognition-Driven Image Restoration network for multiple degradation, dubbed VRD-IR, to recover high-quality images from various unknown corruption types from the perspective of visual recognition within one model. Concretely, we harmonize the semantic representations of diverse degraded images into a unified space in a dynamic manner, and then optimize them towards intrinsic semantics recovery. Moreover, a prior-ascribing optimization strategy is introduced to encourage VRD-IR to couple with various downstream recognition tasks better. Our VRD-IR is corruption- and recognition-agnostic, and can be inserted into various recognition tasks directly as an image enhancement module. Extensive experiments on multiple image distortions demonstrate that our VRD-IR surpasses existing image restoration methods and show superior performance on diverse high-level tasks, including classification, detection, and person re-identification.

Learning to leverage the relationship among diverse image restoration tasks is quite beneficial for unraveling the intrinsicingredients behind the degradation. Recent years have witnessed the flourish of various All-in-one methods, which handle multiple image degradations within a single model. In practice, however, few attempts have been made to excavate task correlations in that exploring the underlying fundamentalingredients of various image degradations, resulting in poor scalability as more tasks are involved. In this paper, we propose a novel perspective to delve into the degradation via aningredients-oriented rather than previous task-oriented manner for scalable learning. Specifically, our method, named Ingredients-oriented Degradation Reformulation framework (IDR), consists of two stages, namely task-oriented knowledge collection and ingredients-oriented knowledge integration. In the first stage, we conduct ad hoc operations on different degradations according to the underlying physics principles, and establish the corresponding prior hubs for each type of degradation. While the second stage progressively reformulates the preceding task-oriented hubs into single ingredients-oriented hub via learnable Principal Component Analysis (PCA), and employs a dynamic routing mechanism for probabilistic unknown degradation removal. Extensive experiments on various image restoration tasks demonstrate the effectiveness and scalability of our method. More importantly, our IDR exhibits the favorable generalization ability to unknown downstream tasks.

Existing deep learning-based computer vision methods usually operate in the spatial and frequency domains, which are two orthogonal \textbf{individual} perspectives for image processing. In this paper, we introduce a new spatial-frequency analysis tool, Fractional Fourier Transform (FRFT), to provide comprehensive \textbf{unified} spatial-frequency perspectives. The FRFT is a unified continuous spatial-frequency transform that simultaneously reflects an image's spatial and frequency representations, making it optimal for processing non-stationary image signals. We explore the properties of the FRFT for image processing and present a fast implementation of the 2D FRFT, which facilitates its widespread use. Based on these explorations, we introduce a simple yet effective operator, Multi-order FRactional Fourier Convolution (MFRFC), which exhibits the remarkable merits of processing images from more perspectives in the spatial-frequency plane. Our proposed MFRFC is a general and basic operator that can be easily integrated into various tasks for performance improvement. We experimentally evaluate the MFRFC on various computer vision tasks, including object detection, image classification, guided super-resolution, denoising, dehazing, deraining, and low-light enhancement. Our proposed MFRFC consistently outperforms baseline methods by significant margins across all tasks.

Deep learning-based source dehazing methods trained on synthetic datasets have achieved remarkable performance but suffer from dramatic performance degradation on real hazy images due to domain shift. Although certain Domain Adaptation (DA) dehazing methods have been presented, they inevitably require access to the source dataset to reduce the gap between the source synthetic and target real domains. To address these issues, we present a novel Source-Free Unsupervised Domain Adaptation (SFUDA) image dehazing paradigm, in which only a well-trained source model and an unlabeled target real hazy dataset are available. Specifically, we devise the Domain Representation Normalization (DRN) module to make the representation of real hazy domain features match that of the synthetic domain to bridge the gaps. With our plug-and-play DRN module, unlabeled real hazy images can adapt existing well-trained source networks. Besides, the unsupervised losses are applied to guide the learning of the DRN module, which consists of frequency losses and physical prior losses. Frequency losses provide structure and style constraints, while the prior loss explores the inherent statistic property of haze-free images. Equipped with our DRN module and unsupervised loss, existing source dehazing models are able to dehaze unlabeled real hazy images. Extensive experiments on multiple baselines demonstrate the validity and superiority of our method visually and quantitatively.

Pan-sharpening aims to generate high-spatial resolution multi-spectral (MS) image by fusing high-spatial resolution panchromatic (PAN) image and its corresponding low-spatial resolution MS image. Despite the remarkable progress, most existing pan-sharpening methods only work in the spatial domain and rarely explore the potential solutions in the frequency domain. In this paper, we propose a novel pan-sharpening framework by adaptively learning low-high frequency information integration in the spatial and frequency dual domains. It consists of three key designs: mask prediction sub-network, low-frequency learning sub-network and high-frequency learning sub-network. Specifically, the first is responsible for measuring the modality-aware frequency information difference of PAN and MS images and further predicting the low-high frequency boundary in the form of a two-dimensional mask. In view of the mask, the second adaptively picks out the corresponding low-frequency components of different modalities and then restores the expected low-frequency one by spatial and frequency dual domains information integration while the third combines the above refined low-frequency and the original high-frequency for the latent high-frequency reconstruction. In this way, the low-high frequency information is adaptively learned, thus leading to the pleasing results. Extensive experiments validate the effectiveness of the proposed network and demonstrate the favorable performance against other state-of-the-art methods. The source code will be released at https://github.com/manman1995/pansharpening.

Existing convolutional neural networks widely adopt spatial down-/up-sampling for multi-scale modeling. However, spatial up-sampling operators (e.g., interpolation, transposed convolution, and un-pooling) heavily depend on local pixel attention, incapably exploring the global dependency. In contrast, the Fourier domain is in accordance with the nature of global modeling according to the spectral convolution theorem. Unlike the spatial domain that easily performs up-sampling with the property of local similarity, up-sampling in the Fourier domain is more challenging as it does not follow such a local property. In this study, we propose a theoretically feasible Deep Fourier Up-Sampling (FourierUp) to solve these issues. We revisit the relationships between spatial and Fourier domains and reveal the transform rules on the features of different resolutions in the Fourier domain, which provide key insights for FourierUp's designs. FourierUp as a generic operator consists of three key components: 2D discrete Fourier transform, Fourier dimension increase rules, and 2D inverse Fourier transform, which can be directly integrated with existing networks. Extensive experiments across multiple computer vision tasks, including object detection, image segmentation, image de-raining, image dehazing, and guided image super-resolution, demonstrate the consistent performance gains obtained by introducing our FourierUp. Code will be publicly available.