Process-Supervised LLM Recommenders via Flow-guided Tuning

While large language models (LLMs) are increasingly adapted for recommendation systems via supervised fine-tuning (SFT), this approach amplifies popularity bias due to its likelihood maximization objective, compromising recommendation diversity and fairness. To address this, we present Flow-guided fine-tuning recommender (Flower), which replaces SFT with a Generative Flow Network (GFlowNet) framework that enacts process supervision through token-level reward propagation. Flower's key innovation lies in decomposing item-level rewards into constituent token rewards, enabling direct alignment between token generation probabilities and their reward signals. This mechanism achieves three critical advancements: (1) popularity bias mitigation and fairness enhancement through empirical distribution matching, (2) preservation of diversity through GFlowNet's proportional sampling, and (3) flexible integration of personalized preferences via adaptable token rewards. Experiments demonstrate Flower's superior distribution-fitting capability and its significant advantages over traditional SFT in terms of fairness, diversity, and accuracy, highlighting its potential to improve LLM-based recommendation systems. The implementation is available via https://github.com/Mr-Peach0301/Flower

SPRec: Leveraging Self-Play to Debias Preference Alignment for Large Language Model-based Recommendations

Large language models (LLMs) have attracted significant attention in recommendation systems. Current LLM-based recommender systems primarily rely on supervised fine-tuning (SFT) to train the model for recommendation tasks. However, relying solely on positive samples limits the model's ability to align with user satisfaction and expectations. To address this, researchers have introduced Direct Preference Optimization (DPO), which explicitly aligns recommendations with user preferences using offline preference ranking data. Despite its advantages, our theoretical analysis reveals that DPO inherently biases the model towards a few items, exacerbating the filter bubble issue and ultimately degrading user experience. In this paper, we propose SPRec, a novel self-play recommendation framework designed to mitigate over-recommendation and improve fairness without requiring additional data or manual intervention. In each self-play iteration, the model undergoes an SFT step followed by a DPO step, treating offline interaction data as positive samples and the predicted outputs from the previous iteration as negative samples. This effectively re-weights the DPO loss function using the model's logits, adaptively suppressing biased items. Extensive experiments on multiple real-world datasets demonstrate SPRec's effectiveness in enhancing recommendation accuracy and addressing fairness concerns.

Learning thin deformable object manipulation with a multi-sensory integrated soft hand

Robotic manipulation has made significant advancements, with systems demonstrating high precision and repeatability. However, this remarkable precision often fails to translate into efficient manipulation of thin deformable objects. Current robotic systems lack imprecise dexterity, the ability to perform dexterous manipulation through robust and adaptive behaviors that do not rely on precise control. This paper explores the singulation and grasping of thin, deformable objects. Here, we propose a novel solution that incorporates passive compliance, touch, and proprioception into thin, deformable object manipulation. Our system employs a soft, underactuated hand that provides passive compliance, facilitating adaptive and gentle interactions to dexterously manipulate deformable objects without requiring precise control. The tactile and force/torque sensors equipped on the hand, along with a depth camera, gather sensory data required for manipulation via the proposed slip module. The manipulation policies are learned directly from raw sensory data via model-free reinforcement learning, bypassing explicit environmental and object modeling. We implement a hierarchical double-loop learning process to enhance learning efficiency by decoupling the action space. Our method was deployed on real-world robots and trained in a self-supervised manner. The resulting policy was tested on a variety of challenging tasks that were beyond the capabilities of prior studies, ranging from displaying suit fabric like a salesperson to turning pages of sheet music for violinists.

Training Over a Distribution of Hyperparameters for Enhanced Performance and Adaptability on Imbalanced Classification

Although binary classification is a well-studied problem, training reliable classifiers under severe class imbalance remains a challenge. Recent techniques mitigate the ill effects of imbalance on training by modifying the loss functions or optimization methods. We observe that different hyperparameter values on these loss functions perform better at different recall values. We propose to exploit this fact by training one model over a distribution of hyperparameter values--instead of a single value--via Loss Conditional Training (LCT). Experiments show that training over a distribution of hyperparameters not only approximates the performance of several models but actually improves the overall performance of models on both CIFAR and real medical imaging applications, such as melanoma and diabetic retinopathy detection. Furthermore, training models with LCT is more efficient because some hyperparameter tuning can be conducted after training to meet individual needs without needing to retrain from scratch.

DLCRec: A Novel Approach for Managing Diversity in LLM-Based Recommender Systems

The integration of Large Language Models (LLMs) into recommender systems has led to substantial performance improvements. However, this often comes at the cost of diminished recommendation diversity, which can negatively impact user satisfaction. To address this issue, controllable recommendation has emerged as a promising approach, allowing users to specify their preferences and receive recommendations that meet their diverse needs. Despite its potential, existing controllable recommender systems frequently rely on simplistic mechanisms, such as a single prompt, to regulate diversity-an approach that falls short of capturing the full complexity of user preferences. In response to these limitations, we propose DLCRec, a novel framework designed to enable fine-grained control over diversity in LLM-based recommendations. Unlike traditional methods, DLCRec adopts a fine-grained task decomposition strategy, breaking down the recommendation process into three sequential sub-tasks: genre prediction, genre filling, and item prediction. These sub-tasks are trained independently and inferred sequentially according to user-defined control numbers, ensuring more precise control over diversity. Furthermore, the scarcity and uneven distribution of diversity-related user behavior data pose significant challenges for fine-tuning. To overcome these obstacles, we introduce two data augmentation techniques that enhance the model's robustness to noisy and out-of-distribution data. These techniques expose the model to a broader range of patterns, improving its adaptability in generating recommendations with varying levels of diversity. Our extensive empirical evaluation demonstrates that DLCRec not only provides precise control over diversity but also outperforms state-of-the-art baselines across multiple recommendation scenarios.

Beyond Full Label: Single-Point Prompt for Infrared Small Target Label Generation

In this work, we make the first attempt to construct a learning-based single-point annotation paradigm for infrared small target label generation (IRSTLG). Our intuition is that label generation requires just one more point prompt than target detection: IRSTLG can be regarded as an infrared small target detection (IRSTD) task with the target location hint. Based on this insight, we introduce an energy double guided single-point prompt (EDGSP) framework, which adeptly transforms the target detection network into a refined label generation method. Specifically, the proposed EDGSP includes: 1) target energy initialization (TEI) to create a foundational outline for sufficient shape evolution of pseudo label, 2) double prompt embedding (DPE) for rapid localization of interested regions and reinforcement of individual differences to avoid label adhesion, and 3) bounding box-based matching (BBM) to eliminate false alarms. Experimental results show that pseudo labels generated by three baselines equipped with EDGSP achieve 100% object-level probability of detection (Pd) and 0% false-alarm rate (Fa) on SIRST, NUDT-SIRST, and IRSTD-1k datasets, with a pixel-level intersection over union (IoU) improvement of 13.28% over state-of-the-art label generation methods. Additionally, the downstream detection task reveals that our centroid-annotated pseudo labels surpass full labels, even with coarse single-point annotations, it still achieves 99.5% performance of full labeling.

A$^{2}$-MAE: A spatial-temporal-spectral unified remote sensing pre-training method based on anchor-aware masked autoencoder

Vast amounts of remote sensing (RS) data provide Earth observations across multiple dimensions, encompassing critical spatial, temporal, and spectral information which is essential for addressing global-scale challenges such as land use monitoring, disaster prevention, and environmental change mitigation. Despite various pre-training methods tailored to the characteristics of RS data, a key limitation persists: the inability to effectively integrate spatial, temporal, and spectral information within a single unified model. To unlock the potential of RS data, we construct a Spatial-Temporal-Spectral Structured Dataset (STSSD) characterized by the incorporation of multiple RS sources, diverse coverage, unified locations within image sets, and heterogeneity within images. Building upon this structured dataset, we propose an Anchor-Aware Masked AutoEncoder method (A$^{2}$-MAE), leveraging intrinsic complementary information from the different kinds of images and geo-information to reconstruct the masked patches during the pre-training phase. A$^{2}$-MAE integrates an anchor-aware masking strategy and a geographic encoding module to comprehensively exploit the properties of RS images. Specifically, the proposed anchor-aware masking strategy dynamically adapts the masking process based on the meta-information of a pre-selected anchor image, thereby facilitating the training on images captured by diverse types of RS sources within one model. Furthermore, we propose a geographic encoding method to leverage accurate spatial patterns, enhancing the model generalization capabilities for downstream applications that are generally location-related. Extensive experiments demonstrate our method achieves comprehensive improvements across various downstream tasks compared with existing RS pre-training methods, including image classification, semantic segmentation, and change detection tasks.

FUSU: A Multi-temporal-source Land Use Change Segmentation Dataset for Fine-grained Urban Semantic Understanding

Fine urban change segmentation using multi-temporal remote sensing images is essential for understanding human-environment interactions. Despite advances in remote sensing data for urban monitoring, coarse-grained classification systems and the lack of continuous temporal observations hinder the application of deep learning to urban change analysis. To address this, we introduce FUSU, a multi-source, multi-temporal change segmentation dataset for fine-grained urban semantic understanding. FUSU features the most detailed land use classification system to date, with 17 classes and 30 billion pixels of annotations. It includes bi-temporal high-resolution satellite images with 20-50 cm ground sample distance and monthly optical and radar satellite time series, covering 847 km2 across five urban areas in China. The fine-grained pixel-wise annotations and high spatial-temporal resolution data provide a robust foundation for deep learning models to understand urbanization and land use changes. To fully leverage FUSU, we propose a unified time-series architecture for both change detection and segmentation and benchmark FUSU on various methods for several tasks. Dataset and code will be available at: https://github.com/yuanshuai0914/FUSU.

UnSAMFlow: Unsupervised Optical Flow Guided by Segment Anything Model

Traditional unsupervised optical flow methods are vulnerable to occlusions and motion boundaries due to lack of object-level information. Therefore, we propose UnSAMFlow, an unsupervised flow network that also leverages object information from the latest foundation model Segment Anything Model (SAM). We first include a self-supervised semantic augmentation module tailored to SAM masks. We also analyze the poor gradient landscapes of traditional smoothness losses and propose a new smoothness definition based on homography instead. A simple yet effective mask feature module has also been added to further aggregate features on the object level. With all these adaptations, our method produces clear optical flow estimation with sharp boundaries around objects, which outperforms state-of-the-art methods on both KITTI and Sintel datasets. Our method also generalizes well across domains and runs very efficiently.

HaVTR: Improving Video-Text Retrieval Through Augmentation Using Large Foundation Models

While recent progress in video-text retrieval has been driven by the exploration of powerful model architectures and training strategies, the representation learning ability of video-text retrieval models is still limited due to low-quality and scarce training data annotations. To address this issue, we present a novel video-text learning paradigm, HaVTR, which augments video and text data to learn more generalized features. Specifically, we first adopt a simple augmentation method, which generates self-similar data by randomly duplicating or dropping subwords and frames. In addition, inspired by the recent advancement in visual and language generative models, we propose a more powerful augmentation method through textual paraphrasing and video stylization using large language models (LLMs) and visual generative models (VGMs). Further, to bring richer information into video and text, we propose a hallucination-based augmentation method, where we use LLMs and VGMs to generate and add new relevant information to the original data. Benefiting from the enriched data, extensive experiments on several video-text retrieval benchmarks demonstrate the superiority of HaVTR over existing methods.