U-Net has become a cornerstone in various visual applications such as image segmentation and diffusion probability models. While numerous innovative designs and improvements have been introduced by incorporating transformers or MLPs, the networks are still limited to linearly modeling patterns as well as the deficient interpretability. To address these challenges, our intuition is inspired by the impressive results of the Kolmogorov-Arnold Networks (KANs) in terms of accuracy and interpretability, which reshape the neural network learning via the stack of non-linear learnable activation functions derived from the Kolmogorov-Anold representation theorem. Specifically, in this paper, we explore the untapped potential of KANs in improving backbones for vision tasks. We investigate, modify and re-design the established U-Net pipeline by integrating the dedicated KAN layers on the tokenized intermediate representation, termed U-KAN. Rigorous medical image segmentation benchmarks verify the superiority of UKAN by higher accuracy even with less computation cost. We further delved into the potential of U-KAN as an alternative U-Net noise predictor in diffusion models, demonstrating its applicability in generating task-oriented model architectures.

Video anomaly detection (VAD) is crucial in scenarios such as surveillance and autonomous driving, where timely detection of unexpected activities is essential. Albeit existing methods have primarily focused on detecting anomalous objects in videos—either by identifying anomalous frames or objects—they often neglect finer-grained analysis, such as anomalous pixels, which limits their ability to capture a broader range of anomalies. To address this challenge, we propose an innovative VAD framework called Track Any Anomalous Object (TAO), which introduces a Granular Video Anomaly Detection Framework that, for the first time, integrates the detection of multiple fine-grained anomalous objects into a unified framework. Unlike methods that assign anomaly scores to every pixel at each moment, our approach transforms the problem into pixel-level tracking of anomalous objects. By linking anomaly scores to subsequent tasks such as image segmentation and video tracking, our method eliminates the need for threshold selection and achieves more precise anomaly localization, even in long and challenging video sequences. Experiments on extensive datasets demonstrate that TAO achieves state-of-the-art performance, setting a new progress for VAD by providing a practical, granular, and holistic solution. For more information, visit the project page at: https://tao-25.github.io/

3D Gaussian splatting (3DGS) has enabled various applications in 3D scene representation and novel view synthesis due to its efficient rendering capabilities. However, 3DGS demands relatively significant GPU memory, limiting its use on devices with restricted computational resources. Previous approaches have focused on pruning less important Gaussians, effectively compressing 3DGS but often requiring a fine-tuning stage and lacking adaptability for the specific memory needs of different devices. In this work, we present an elastic inference method for 3DGS. Given an input for the desired model size, our method selects and transforms a subset of Gaussians, achieving substantial rendering performance without additional fine-tuning. We introduce a tiny learnable module that controls Gaussian selection based on the input percentage, along with a transformation module that adjusts the selected Gaussians to complement the performance of the reduced model. Comprehensive experiments on ZipNeRF, MipNeRF and Tanks&Temples scenes demonstrate the effectiveness of our approach. Code is available at https://flexgs.github.io/.

Out-of-distribution (OOD) detection remains challenging for deep learning models, particularly when test-time OOD samples differ significantly from training outliers. We propose OODD, a novel test-time OOD detection method that dynamically maintains and updates an OOD dictionary without fine-tuning. Our approach leverages a priority queue-based dictionary that accumulates representative OOD features during testing, combined with an informative inlier sampling strategy for in-distribution (ID) samples. To ensure stable performance during early testing, we propose a dual OOD stabilization mechanism that leverages strategically generated outliers derived from ID data. To our best knowledge, extensive experiments on the OpenOOD benchmark demonstrate that OODD significantly outperforms existing methods, achieving a 26.0% improvement in FPR95 on CIFAR-100 Far OOD detection compared to the state-of-the-art approach. Furthermore, we present an optimized variant of the KNN-based OOD detection framework that achieves a 3x speedup while maintaining detection performance. Our code is available at https://github.com/zxk1212/OODD.

As 3D Gaussian Splatting (3D-GS) emerges as a promising technique for 3D reconstruction and novel view synthesis, offering superior rendering quality and efficiency, it becomes crucial to ensure secure transmission and copyright protection of 3D assets in anticipation of widespread distribution. While steganography has advanced significantly in common 3D media like meshes and Neural Radiance Fields (NeRF), research into steganography for 3D- GS representations remains largely unexplored. To address this gap, we propose ConcealGS, a novel 3D steganography method that embeds implicit information into the explicit 3D representation of Gaussian Splatting. By introducing a consistency strategy for the decoder and a gradient optimization approach, ConcealGS overcomes limitations of NeRF-based models, enhancing both the robustness of implicit information and the quality of 3D reconstruction. Extensive evaluations across various potential application scenarios demonstrate that ConcealGS successfully recovers implicit information with negligible impact on rendering quality, offering a groundbreaking approach for embedding invisible yet recoverable information into 3D models. This work paves the way for advanced copyright protection and secure data transmission in the evolving landscape of 3D content creation and distribution. Code is available at https://github.com/zxk1212/ConcealGS.

With the rapid development of large generative models for 3D, especially the evolution from NeRF representations to more efficient Gaussian Splatting, the synthesis of 3D assets has become increasingly fast and efficient, enabling the large-scale publication and sharing of generated 3D objects. However, while existing methods can add watermarks or steganographic information to individual 3D assets, they often require time-consuming per-scene training and optimization, leading to watermarking overheads that can far exceed the time required for asset generation itself, making deployment impractical for generating large collections of 3D objects. To address this, we propose InstantSplamp a framework that seamlessly integrates the 3D steganography pipeline into large 3D generative models without introducing explicit additional time costs. Guided by visual foundation models,InstantSplamp subtly injects hidden information like copyright tags during asset generation, enabling effective embedding and recovery of watermarks within generated 3D assets while preserving original visual quality. Experiments across various potential deployment scenarios demonstrate that \model~strikes an optimal balance between rendering quality and hiding fidelity, as well as between hiding performance and speed. Compared to existing per-scene optimization techniques for 3D assets, InstantSplamp reduces their watermarking training overheads that are multiples of generation time to nearly zero, paving the way for real-world deployment at scale. Project page: https://gaussian-stego.github.io/.

As 4D extensions of 3D Gaussian Splatting (4D-GS) emerge as groundbreaking techniques for dynamic scene reconstruction and novel view synthesis in robotics and computer vision, ensuring the security and trustworthiness of these assets becomes crucial. While steganography has advanced significantly in 2D and 3D media, existing methods are inadequate for the complex, dynamic nature of 4D-GS representations. To address this gap, we propose Hide-in-Motion, a novel 4D steganography method for hiding information through deformation in Gaussian splatting. Our approach introduces a composite attribute and a Decouple Feature Field for coarse-to-fine deformation modeling and embedding implicit information, along with an Opacity-Guided Adaptive strategy. Hide-in-Motion overcomes the limitations of previous techniques, enhancing both the robustness of embedded information and the quality of 4D reconstruction. Extensive evaluations demonstrate that our method successfully embeds and recovers implicit information across various modalities while maintaining high rendering quality in dynamic scenes. This work not only advances copyright protection and secure data transmission for 4D assets but also paves the way for enhancing the security and integrity of 4D digital assets. Code is available at https://github.com/CUHK-AIM-Group/Hide-in-Motion.

Vision-language models (VLMs) excel at descriptive tasks, but whether they truly understand scenes from visual observations remains uncertain. We introduce IR3D-Bench, a benchmark challenging VLMs to demonstrate understanding through active creation rather than passive recognition. Grounded in the analysis-by-synthesis paradigm, IR3D-Bench tasks Vision-Language Agents (VLAs) with actively using programming and rendering tools to recreate the underlying 3D structure of an input image, achieving agentic inverse rendering through tool use. This ''understanding-by-creating'' approach probes the tool-using generative capacity of VLAs, moving beyond the descriptive or conversational capacity measured by traditional scene understanding benchmarks. We provide a comprehensive suite of metrics to evaluate geometric accuracy, spatial relations, appearance attributes, and overall plausibility. Initial experiments on agentic inverse rendering powered by various state-of-the-art VLMs highlight current limitations, particularly in visual precision rather than basic tool usage. IR3D-Bench, including data and evaluation protocols, is released to facilitate systematic study and development of tool-using VLAs towards genuine scene understanding by creating.

Reconstructing deformable anatomical structures from endoscopic videos is a pivotal and promising research topic that can enable advanced surgical applications and improve patient outcomes. While existing surgical scene reconstruction methods have made notable progress, they often suffer from slow rendering speeds due to using neural radiance fields, limiting their practical viability in real-world applications. To overcome this bottleneck, we propose EndoGaussian, a framework that integrates the strengths of 3D Gaussian Splatting representations, allowing for high-fidelity tissue reconstruction, efficient training, and real-time rendering. Specifically, we dedicate a Foundation Model-driven Initialization (FMI) module, which distills 3D cues from multiple vision foundation models (VFMs) to swiftly construct the preliminary scene structure for Gaussian initialization. Then, a Spatio-temporal Gaussian Tracking (SGT) is designed, efficiently modeling scene dynamics using the multi-scale HexPlane with spatio-temporal priors. Furthermore, to improve the dynamics modeling ability for scenes with large deformation, EndoGaussian integrates Motion-aware Frame Synthesis (MFS) to adaptively synthesize new frames as extra training constraints. Experimental results on public datasets demonstrate EndoGaussian's efficacy against prior state-of-the-art methods, including superior rendering speed (168 FPS, real-time), enhanced rendering quality (38.555 PSNR), and reduced training overhead (within 2 min/scene). These results underscore EndoGaussian's potential to significantly advance intraoperative surgery applications, paving the way for more accurate and efficient real-time surgical guidance and decision-making in clinical scenarios. Code is available at: https://github.com/CUHK-AIM-Group/EndoGaussian.

Generating diverse plausible outputs from a single input is crucial for addressing visual ambiguities, exemplified in medical imaging where experts may provide varying semantic segmentation annotations for the same image.Existing methods handles ambiguous segmentation relying on probabilistic modeling and extensive multi-output annotated data while often struggles with limited ambiguously labeled datasets common in real-world applications.To surmount the challenge, we propose P²SAM, a novel framework that leverages the Segment Anything Model (SAM)'s prior knowledge for ambiguous object segmentation. By transforming SAM's sensitivity to prompts into an advantage, we introduce a prior probabilistic space for prompts.Experimental results show that P²SAM significantly enhances medical segmentation precision and diversity using minimal ambiguously annotated samples. Benchmarking against state-of-the-art methods demonstrates superior performance with just 5.5% of the training data (+12% Dmax). This approach marks a significant advancement towards deploying probabilistic models in data-limited real-world scenarios.

As the vision foundation models like the Segment Anything Model (SAM) demonstrate potent universality, they also present challenges in giving ambiguous and uncertain predictions. Significant variations in the model output and granularity can occur with simply subtle changes in the prompt, contradicting the consensus requirement for the robustness of a model. While some established works have been dedicated to stabilizing and fortifying the prediction of SAM, this paper takes a unique path to explore how this flaw can be inverted into an advantage when modeling inherently ambiguous data distributions. We introduce an optimization framework based on a conditional variational autoencoder, which jointly models the prompt and the granularity of the object with a latent probability distribution. This approach enables the model to adaptively perceive and represent the real ambiguous label distribution, taming SAM to produce a series of diverse, convincing, and reasonable segmentation outputs controllably. Extensive experiments on several practical deployment scenarios involving ambiguity demonstrates the exceptional performance of our framework. Project page: \url{https://a-sa-m.github.io/}.