In this work, we propose using a unified representation, termed **Factorized Features**, for low-level vision tasks, where we test on **Single Image Super-Resolution (SISR)** and **Image Compression**. Motivated by the shared principles between these tasks, they require recovering and preserving fine image details, whether by enhancing resolution for SISR or reconstructing compressed data for Image Compression. Unlike previous methods that mainly focus on network architecture, our proposed approach utilizes a basis-coefficient decomposition as well as an explicit formulation of frequencies to capture structural components and multi-scale visual features in images, which addresses the core challenges of both tasks. We replace the representation of prior models from simple feature maps with Factorized Features to validate the potential for broad generalizability. In addition, we further optimize the compression pipeline by leveraging the mergeable-basis property of our Factorized Features, which consolidates shared structures on multi-frame compression. Extensive experiments show that our unified representation delivers state-of-the-art performance, achieving an average relative improvement of 204.4\% in PSNR over the baseline in Super-Resolution (SR) and 9.35\% BD-rate reduction in Image Compression compared to the previous SOTA.

Recent text-to-image (T2I) diffusion models have revolutionized image editing by empowering users to control out-comes using natural language. However, the ease of image manipulation has raised ethical concerns, with the poten-tial for malicious use in generating deceptive or harmful content. To address the concerns, we propose an image im-munization approach named semantic attack to protect our images from being manipulated by malicious agents using diffusion models. Our approach focuses on disrupting the semantic understanding of T2I diffusion models regarding specific content. By attacking the cross-attention mecha-nism that encodes image features with text messages during editing, we distract the model's attention regarding the con-tent of our concern. Our semantic attack renders the model uncertain about the areas to edit, resulting in poorly edited images and contradicting the malicious editing attempts. In addition, by shifting the attack target towards intermediate attention maps from the final generated image, our approach substantially diminishes computational burden and alleviates GPU memory constraints in comparison to pre-vious methods. Moreover, we introduce timestep universal gradient updating to create timestep-agnostic perturbations effective across different input noise levels. By treating the full diffusion process as discrete denoising timesteps during the attack, we achieve equivalent or even superior immu-nization efficacy with nearly half the memory consumption of the previous method. Our contributions include a prac-tical and effective approach to safeguard images against malicious editing, and the proposed method offers robust immunization against various image inpainting and editing approaches, showcasing its potential for real-world appli-cations.

Large Pre-trained Transformers exhibit an intriguing capacity for in-context learning. Without gradient updates, these models can rapidly construct new predictors from demonstrations presented in the inputs. Recent works promote this ability in the vision-language domain by incorporating visual information into large language models that can already make in-context predictions. However, these methods could inherit issues in the language domain, such as template sensitivity and hallucination. Also, the scale of these language models raises a significant demand for computations, making learning and operating these models resource-intensive. To this end, we raise a question: "How can we enable in-context learning without relying on the intrinsic in-context ability of large language models?". To answer it, we propose a succinct and general framework, Self-supervised IN-Context learning (SINC), that introduces a meta-model to learn on self-supervised prompts consisting of tailored demonstrations. The learned models can be transferred to downstream tasks for making in-context predictions on-the-fly. Extensive experiments show that SINC outperforms gradient-based methods in various vision-language tasks under few-shot settings. Furthermore, the designs of SINC help us investigate the benefits of in-context learning across different tasks, and the analysis further reveals the essential components for the emergence of in-context learning in the vision-language domain.