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/

Vision-centric perception systems struggle with unpredictable and coupled weather degradations in the wild. Current solutions are often limited, as they either depend on specific degradation priors or suffer from significant domain gaps. To enable robust and autonomous operation in real-world conditions, we propose JarvisIR, a VLM-powered agent that leverages the VLM as a controller to manage multiple expert restoration models. To further enhance system robustness, reduce hallucinations, and improve generalizability in real-world adverse weather, JarvisIR employs a novel two-stage framework consisting of supervised fine-tuning and human feedback alignment. Specifically, to address the lack of paired data in real-world scenarios, the human feedback alignment enables the VLM to be fine-tuned effectively on large-scale real-world data in an unsupervised manner. To support the training and evaluation of JarvisIR, we introduce CleanBench, a comprehensive dataset consisting of high-quality and large-scale instruction-responses pairs, including 150K synthetic entries and 80K real entries. Extensive experiments demonstrate that JarvisIR exhibits superior decision-making and restoration capabilities. Compared with existing methods, it achieves a 50% improvement in the average of all perception metrics on CleanBench-Real.

Recently, deep learning-based pan-sharpening algorithms have achieved notable advancements over traditional methods. However, deep learning-based methods incur substantial computational overhead during inference, especially with large images. This excessive computational demand limits the applicability of these methods in real-world scenarios, particularly in the absence of dedicated computing devices such as GPUs and TPUs. To address these challenges, we propose Pan-LUT, a novel learnable look-up table (LUT) framework for pan-sharpening that strikes a balance between performance and computational efficiency for large remote sensing images. Our method makes it possible to process 15K$\times$15K remote sensing images on a 24GB GPU. To finely control the spectral transformation, we devise the PAN-guided look-up table (PGLUT) for channel-wise spectral mapping. To effectively capture fine-grained spatial details, we introduce the spatial details look-up table (SDLUT). Furthermore, to adaptively aggregate channel information for generating high-resolution multispectral images, we design an adaptive output look-up table (AOLUT). Our model contains fewer than 700K parameters and processes a 9K$\times$9K image in under 1 ms using one RTX 2080 Ti GPU, demonstrating significantly faster performance compared to other methods. Experiments reveal that Pan-LUT efficiently processes large remote sensing images in a lightweight manner, bridging the gap to real-world applications. Furthermore, our model surpasses SOTA methods in full-resolution scenes under real-world conditions, highlighting its effectiveness and efficiency. We also extend our method to general image fusion tasks.

Photo retouching has become integral to contemporary visual storytelling, enabling users to capture aesthetics and express creativity. While professional tools such as Adobe Lightroom offer powerful capabilities, they demand substantial expertise and manual effort. In contrast, existing AI-based solutions provide automation but often suffer from limited adjustability and poor generalization, failing to meet diverse and personalized editing needs. To bridge this gap, we introduce JarvisArt, a multi-modal large language model (MLLM)-driven agent that understands user intent, mimics the reasoning process of professional artists, and intelligently coordinates over 200 retouching tools within Lightroom. JarvisArt undergoes a two-stage training process: an initial Chain-of-Thought supervised fine-tuning to establish basic reasoning and tool-use skills, followed by Group Relative Policy Optimization for Retouching (GRPO-R) to further enhance its decision-making and tool proficiency. We also propose the Agent-to-Lightroom Protocol to facilitate seamless integration with Lightroom. To evaluate performance, we develop MMArt-Bench, a novel benchmark constructed from real-world user edits. JarvisArt demonstrates user-friendly interaction, superior generalization, and fine-grained control over both global and local adjustments, paving a new avenue for intelligent photo retouching. Notably, it outperforms GPT-4o with a 60\% improvement in average pixel-level metrics on MMArt-Bench for content fidelity, while maintaining comparable instruction-following capabilities.

Deep learning-driven synthetic aperture radar automatic target recognition (SAR ATR) has gained increasing attention recently. However, current methods remain highly vulnerable to adversarial attacks, limiting their practical application. Most adversarial defense methods rely on adversarial training or attack detection, which tend to overfit and result in poor robustness against different attack types. Moreover, these methods show limited resilience to query-based attacks, where attackers iteratively probe the model to identify vulnerabilities and generate precise adversarial samples. To overcome these challenges, we propose a random response-based SAR purification defense (RRPD) framework composed of two key components: a diffusion purification module (DPM) and a multiexpert randomized response module (MRRM). The DPM removes adversarial noise through diffusion denoising while preserving critical information for accurate recognition. The MRRM introduces multiple expert models to increase response randomness, thus reducing the effectiveness of query-based attacks. Experimental results show that the proposed framework significantly enhances robustness against adversarial attacks on public SAR datasets, improving system security and reliability. The code is available athttps://github.com/SmartDSP2024/RRPD.

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.