DSPO: Direct Semantic Preference Optimization for Real-World Image Super-Resolution

Recent advances in diffusion models have improved Real-World Image Super-Resolution (Real-ISR), but existing methods lack human feedback integration, risking misalignment with human preference and may leading to artifacts, hallucinations and harmful content generation. To this end, we are the first to introduce human preference alignment into Real-ISR, a technique that has been successfully applied in Large Language Models and Text-to-Image tasks to effectively enhance the alignment of generated outputs with human preferences. Specifically, we introduce Direct Preference Optimization (DPO) into Real-ISR to achieve alignment, where DPO serves as a general alignment technique that directly learns from the human preference dataset. Nevertheless, unlike high-level tasks, the pixel-level reconstruction objectives of Real-ISR are difficult to reconcile with the image-level preferences of DPO, which can lead to the DPO being overly sensitive to local anomalies, leading to reduced generation quality. To resolve this dichotomy, we propose Direct Semantic Preference Optimization (DSPO) to align instance-level human preferences by incorporating semantic guidance, which is through two strategies: (a) semantic instance alignment strategy, implementing instance-level alignment to ensure fine-grained perceptual consistency, and (b) user description feedback strategy, mitigating hallucinations through semantic textual feedback on instance-level images. As a plug-and-play solution, DSPO proves highly effective in both one-step and multi-step SR frameworks.

Graph-Structured Driven Dual Adaptation for Mitigating Popularity Bias

Popularity bias challenges recommender systems by causing uneven recommendation performance and amplifying the Matthew effect. Limited user-item interactions confine unpopular items within embedding neighborhoods of few users, leading to representation collapse and reduced model generalization. Existing supervised alignment and reweighting methods mitigate this bias but have key limitations: (1) ignoring inherent variability across Graph Convolutional Networks (GCNs) layers, causing negative effects in deeper layers; (2) reliance on fixed hyperparameters to balance item popularity, restricting adaptability and increasing complexity. To address these issues, we propose the Graph-Structured Dual Adaptation Framework (GSDA). Our theoretical analysis identifies a crucial limitation of supervised alignment methods caused by over-smoothing in GCNs. As GCN layers deepen, popular and unpopular items increasingly lose distinctiveness, quantified by reduced conditional entropy. This diminished distinctiveness weakens supervised alignment effectiveness in mitigating popularity bias. Motivated by this, GSDA captures structural and distribution characteristics from the adjacency matrix through a dual adaptive strategy. First, a hierarchical adaptive alignment mechanism uses the adjacency matrix's Frobenius norm for layer-specific weight decay, countering conditional entropy reduction effects at deeper layers. Second, a distribution-aware dynamic contrast weighting strategy, guided by a real-time Gini coefficient, removes dependence on fixed hyperparameters, enabling adaptability to diverse data. Experiments on three benchmark datasets demonstrate GSDA significantly alleviates popularity bias and consistently outperforms state-of-the-art recommendation methods.

Autoregressive Image Generation Guided by Chains of Thought

In the field of autoregressive (AR) image generation, models based on the 'next-token prediction' paradigm of LLMs have shown comparable performance to diffusion models by reducing inductive biases. However, directly applying LLMs to complex image generation can struggle with reconstructing the structure and details of the image, impacting the accuracy and stability of generation. Additionally, the 'next-token prediction' paradigm in the AR model does not align with the contextual scanning and logical reasoning processes involved in human visual perception, limiting effective image generation. Chain-of-Thought (CoT), as a key reasoning capability of LLMs, utilizes reasoning prompts to guide the model, improving reasoning performance on complex natural language process (NLP) tasks, enhancing accuracy and stability of generation, and helping the model maintain contextual coherence and logical consistency, similar to human reasoning. Inspired by CoT from the field of NLP, we propose autoregressive Image Generation with Thoughtful Reasoning (IGTR) to enhance autoregressive image generation. IGTR adds reasoning prompts without modifying the model structure or raster generation order. Specifically, we design specialized image-related reasoning prompts for AR image generation to simulate the human reasoning process, which enhances contextual reasoning by allowing the model to first perceive overall distribution information before generating the image, and improve generation stability by increasing the inference steps. Compared to the AR method without prompts, our method shows outstanding performance and achieves an approximate improvement of 20%.

Popularity-Aware Alignment and Contrast for Mitigating Popularity Bias

Collaborative Filtering (CF) typically suffers from the significant challenge of popularity bias due to the uneven distribution of items in real-world datasets. This bias leads to a significant accuracy gap between popular and unpopular items. It not only hinders accurate user preference understanding but also exacerbates the Matthew effect in recommendation systems. To alleviate popularity bias, existing efforts focus on emphasizing unpopular items or separating the correlation between item representations and their popularity. Despite the effectiveness, existing works still face two persistent challenges: (1) how to extract common supervision signals from popular items to improve the unpopular item representations, and (2) how to alleviate the representation separation caused by popularity bias. In this work, we conduct an empirical analysis of popularity bias and propose Popularity-Aware Alignment and Contrast (PAAC) to address two challenges. Specifically, we use the common supervisory signals modeled in popular item representations and propose a novel popularity-aware supervised alignment module to learn unpopular item representations. Additionally, we suggest re-weighting the contrastive learning loss to mitigate the representation separation from a popularity-centric perspective. Finally, we validate the effectiveness and rationale of PAAC in mitigating popularity bias through extensive experiments on three real-world datasets. Our code is available at https://github.com/miaomiao-cai2/KDD2024-PAAC.

TrOMR:Transformer-Based Polyphonic Optical Music Recognition

Optical Music Recognition (OMR) is an important technology in music and has been researched for a long time. Previous approaches for OMR are usually based on CNN for image understanding and RNN for music symbol classification. In this paper, we propose a transformer-based approach with excellent global perceptual capability for end-to-end polyphonic OMR, called TrOMR. We also introduce a novel consistency loss function and a reasonable approach for data annotation to improve recognition accuracy for complex music scores. Extensive experiments demonstrate that TrOMR outperforms current OMR methods, especially in real-world scenarios. We also develop a TrOMR system and build a camera scene dataset for full-page music scores in real-world. The code and datasets will be made available for reproducibility.