Diffusion models have gained prominence as powerful generative tools for solving inverse problems due to their ability to model complex data distributions. However, existing methods typically rely on complete knowledge of the forward observation process to compute gradients for guided sampling, limiting their applicability in scenarios where such information is unavailable. In this work, we introduce *Constrained Particle Seeking (CPS)*, a novel gradient-free approach that leverages all candidate particle information to actively search for the optimal particle while incorporating constraints aligned with high-density regions of the unconditional prior. Unlike previous methods that passively select promising candidates, CPS reformulates the inverse problem as a constrained optimization task, enabling more flexible and efficient particle seeking. We demonstrate that CPS can effectively solve both image and scientific inverse problems, achieving results comparable to gradient-based methods while significantly outperforming gradient-free alternatives.

Spiking Neural Networks (SNNs) promise significant energy efficiency by processing information via sparse, event-driven spikes. However, realizing this potential is hindered by the conventional use of a rigid, uniform timestep, T. This constraint imposes a challenging trade-off between accuracy and latency, while also incurring the prohibitive training costs of Backpropagation Through Time (BPTT). To overcome this limitation, we introduce the Pseudo-Spiking Neuron (PseudoSN), a novel training proxy that conceptualizes latency as an intrinsic, learnable parameter for each neuron. Building on the efficiency of rate-based methods, the PseudoSN models temporal dynamics in a single, BPTT-free pass. It employs a learnable probabilistic noise scheme to emulate the discretization effects of spike generation (e.g., clipping and quantization), making the neuron-specific timestep—and thus latency—directly optimizable via backpropagation. Integrated into a hardware-aware objective, our framework trains heterogeneous-latency SNNs that autonomously learn to optimize the trade-offs among accuracy, latency and energy, establishing a new state-of-the-art on major benchmarks.

Knowledge distillation from Artificial Neural Networks (ANNs) to Spiking Neural Networks (SNNs) is a prominent training paradigm. However, its efficacy is fundamentally limited by a spectral mismatch: SNNs, with their intrinsic low-pass filtering characteristics, struggle to learn high-frequency details from their ANN teachers, creating a bottleneck in knowledge transfer at both the feature and logit levels. To address this, we propose Bi-Spectrum Distillation (BSD), a novel framework that mitigates the mismatch from two complementary perspectives. First, at the feature level, our Spectral Residual Distillation (SRD) enhances the student SNN's features with a parameter-efficient, learnable filter that adaptively compensates for high-frequency information loss, which transforms the student's output to better match the teacher's rich spectral target. Second, at the logits level, our Spectral Semantic Distillation (SSD) enhances fine-grained classification by distilling high-frequency components from teacher-ordered logits. Extensive experiments on CIFAR-10/100, ImageNet, and CIFAR10-DVS demonstrate that BSD achieves new state-of-the-art performance across both CNN and Transformer-based SNNs, validating its effectiveness and broad applicability.

Diffusion models (DMs) have recently demonstrated remarkable success in modeling large-scale data distributions. However, many downstream tasks require guiding the generated content based on specific differentiable metrics, typically necessitating backpropagation during the generation process. This approach is computationally expensive, as generating with DMs often demands tens to hundreds of recursive network calls, resulting in high memory usage and significant time consumption. In this paper, we propose a more efficient alternative that approaches the problem from the perspective of parallel denoising. We show that full backpropagation throughout the entire generation process is unnecessary. The downstream metrics can be optimized by retaining the computational graph of only one step during generation, thus providing a shortcut for gradient propagation. The resulting method, which we call Shortcut Diffusion Optimization (SDO), is generic, high-performance, and computationally lightweight, capable of optimizing all parameter types in diffusion sampling. We demonstrate the effectiveness of SDO on several real-world tasks, including controlling generation by optimizing latent and aligning the DMs by fine-tuning network parameters. Compared to full backpropagation, our approach reduces computational costs by $\sim\! 90\%$∼90% while maintaining superior performance. Code is available at https://github.com/deng-ai-lab/SDO.

Natural image quality is often degraded by adverse weather conditions, significantly impairing the performance of downstream tasks. Image restoration has emerged as a core solution to this challenge and has been widely discussed in the literature. Although recent transformer-based approaches have made remarkable progress in image restoration, their increasing system complexity poses significant challenges for real-time processing, particularly in real-world deployment scenarios. To this end, most existing methods attempt to simplify the self-attention mechanism, such as by channel self-attention or state space model. However, these methods primarily focus on network architecture while neglecting the inherent characteristics of image restoration itself. In this context, we explore a pyramid Wavelet-Fourier iterative pipeline to demonstrate the potential of Wavelet-Fourier processing for image restoration. Inspired by the above findings, we propose a novel and efficient restoration baseline, named Pyramid Wavelet-Fourier Network (PW-FNet). Specifically, PW-FNet features two key design principles: 1) at the inter-block level, integrates a pyramid wavelet-based multi-input multi-output structure to achieve multi-scale and multi-frequency bands decomposition; and 2) at the intra-block level, incorporates Fourier transforms as an efficient alternative to self-attention mechanisms, effectively reducing computational complexity while preserving global modeling capability. Extensive experiments on tasks such as image deraining, raindrop removal, image super-resolution, motion deblurring, image dehazing, image desnowing and underwater/low-light enhancement demonstrate that PW-FNet not only surpasses state-of-the-art methods in restoration quality but also achieves superior efficiency, with significantly reduced parameter size, computational cost and inference time. The code is available at: https://github.com/deng-ai-lab/PW-FNet.

Fine-grained visual referring and grounding are critical for enhancing scene understanding and enabling various real-world vision-language applications. Although recent studies have extended multimodal large language models (MLLMs) to these tasks, they still face significant challenges in fine-grained multi-target scenarios. To address this, we propose MTRAG, a pixel-level multi-target referring and grounding framework that leverages semantic-spatial collaboration. Specifically, we introduce a Channel Extension Mechanism (CEM) that enables a global image encoder to extract global semantics and multi-region representations while retaining background context, without extra region feature extractors. Moreover, we introduce a grounding branch for pixel-level grounding and design a Hybrid Adapter (HA) to fuse semantic features from the MLLM branch with spatial information from the grounding branch, thereby enhancing the semantic-spatial alignment. For training, we meticulously curate MTRAG-D, a dataset comprising single- and multi-target referring and grounding samples derived from existing datasets and newly synthesized free-form multi-target referring instruction-following data. We also present MTR-Bench, a benchmark for systematic evaluation of multi-target referring. Extensive experiments across five core tasks, including single- and multi-target referring and grounding as well as image-level captioning, show that MTRAG consistently outperforms strong baselines on both multi- and single-target tasks, while maintaining competitive image-level understanding. The code is available at https://github.com/deng-ai-lab/MTRAG.

Reconstructing realistic underwater scenes from underwater video remains a meaningful yet challenging task in the multimedia domain. The inherent spatiotemporal degradations in underwater imaging, including caustics, flickering, attenuation, and backscattering, frequently result in inaccurate geometry and appearance in existing 3D reconstruction methods. While a few recent works have explored underwater degradation-aware reconstruction, they often address either spatial or temporal degradation alone, falling short in more real-world underwater scenarios where both types of degradation occur. We propose MarineSTD-GS, a novel 3D Gaussian Splatting-based framework that explicitly models both temporal and spatial degradations for realistic underwater scene reconstruction. Specifically, we introduce two paired Gaussian primitives: Intrinsic Gaussians represent the true scene, while Degraded Gaussians render the degraded observations. The color of each Degraded Gaussian is physically derived from its paired Intrinsic Gaussian via a Spatiotemporal Degradation Modeling (SDM) module, enabling self-supervised disentanglement of realistic appearance from degraded images. To ensure stable training and accurate geometry, we further propose a Depth-Guided Geometry Loss and a Multi-Stage Optimization strategy. We also construct a simulated benchmark with diverse spatial and temporal degradations and ground-truth appearances for comprehensive evaluation. Experiments on both simulated and real-world datasets show that MarineSTD-GS robustly handles spatiotemporal degradations and outperforms existing methods in novel view synthesis with realistic, water-free scene appearances.

Reconstructing underwater 3D scenes with accurate appearances is crucial for numerous tasks. However, existing underwater 3D reconstruction methods often fail to restore the true scene appearance due to degradation in underwater images caused by water effects. In this letter, we propose SeaFree-GS, a novel approach leveraging 3D Gaussian Splatting (3DGS) to reconstruct underwater scenes with their true appearances. Specifically, we introduce a Degradation-Aware Dual-Color Modeling strategy, where each Gaussian is assigned an intrinsic color representing the true scene appearance and a viewpoint-dependent degraded color to incorporate water effects. For a given viewpoint, this strategy physically derives the corresponding degraded colors from the intrinsic colors to render the underwater image. To improve reconstruction accuracy, we introduce a Content-Based Loss for selective enhancement of supervision over foreground and background regions, and a Coarse-Grained Depth Loss to enforce additional geometric constraints. Experiments on three datasets demonstrate that SeaFree-GS achieves state-of-the-art performance in Underwater True Appearance Reconstruction, and also performs competitively in Underwater Novel View Synthesis.

Generative models represented by diffusion models have recently shown great potential in image generation. They usually use a reverse iteration process to map noise into the data. However, for many real-world applications such as image restoration and translation, the model input comes from a distribution that is not random noise, making it difficult for these models to adapt directly to these tasks. In this paper, we introduce Image-to-Image Bayesian Flow Networks (I2I-BFNs), a novel framework for general-purpose image-to-image translation (I2I) that operates within the parameter space of distributions. This method upholds Gaussian distributions over pixel intensities, refining distribution parameters through closed-form Bayesian inference, steered by the network's predictions for the target image. An essential aspect of our approach is the utilization of the conditional image as a robust prior parameter, initializing the translation process from a deterministic, clean image to reduce variance and produce interpretable generation. Additionally, we introduce a skip sampling technique that enhances the efficiency of I2I-BFNs, facilitating rapid translation in diverse image restoration and general I2I tasks. Our experimental evaluations showcase the model's competitive edge in various settings, underscoring its efficacy and adaptability. This work contributes new insights and opportunities for the large-scale development of efficient conditional generation systems.