In the era of information explosion, Recommender Systems (RS) are essential for alleviating information overload and providing personalized user experiences. Recent advances in diffusion-based generative recommenders have shown promise in capturing the dynamic nature of user preferences. These approaches explore a broader range of user interests by progressively perturbing the distribution of user-item interactions and recovering potential preferences from noise, enabling nuanced behavioral understanding. However, existing diffusion-based approaches predominantly operate in continuous space through encoded graph-based historical interactions, which may compromise potential information loss and suffer from computational inefficiency. As such, we propose CDRec, a novel Continuous-time Discrete-space Diffusion Recommendation framework, which models user behavior patterns through discrete diffusion on historical interactions over continuous time. The discrete diffusion algorithm operates via discrete element operations (e.g., masking) while incorporating domain knowledge through transition matrices, producing more meaningful diffusion trajectories. Furthermore, the continuous-time formulation enables flexible adaptive sampling. To better adapt discrete diffusion models to recommendations, CDRec introduces: (1) a novel popularity-aware noise schedule that generates semantically meaningful diffusion trajectories, and (2) an efficient training framework combining consistency parameterization for fast sampling and a contrastive learning objective guided by multi-hop collaborative signals for personalized recommendation. Extensive experiments on real-world datasets demonstrate CDRec's superior performance in both recommendation accuracy and computational efficiency.

In real-world information retrieval scenarios, addressing data incompleteness is crucial for providing accurate similarity scores and ensuring reliable results for downstream tasks. Previous Similarity Matrix Completion (SMC) methods aim to estimate a similarity matrix that exhibits positive semi-definiteness (PSD) from an inaccurate one. However, these methods are inadequate in cases where the initial similarity matrix$S^{0}$exhibits PSD. In this paper, we propose a novel SMC framework that simultaneously explores the symmetric, positive semi-definiteness (PSD), and low-rank properties to provide an accurate and efficient solution for similarity search tasks. Specifically, we exploit the low-rank property and introduce an efficient specialized Cholesky factorization (CF) technique into the conventional SMC framework, which is implemented via a regularizer. It improves computational efficiency by learning a smaller factorized matrix instead of the entire similarity matrix. Moreover, to enhance the optimality guarantees of SMC, we introduce a novel lower-rank matrix property and design two corresponding regularizers. Building upon these meticulous designs, our novel SMC framework, for the first time, ensures both efficiency and accuracy with theoretical guarantees. Consequently, we propose two novel algorithms, i.e., SMCFN/SMCRN, to implement the SMC framework. Theoretical analysis verifies the effectiveness and fast convergence speed of SMCFN/SMCRN. Extensive experiments on five real-world datasets validate our theoretical analysis: SMCFN/SMCRN achieves up to 42% lower RMSE (e.g., 0.34 vs. 0.59 on ImageNet) and 15% higher Recall than state-of-the-art baselines, while being the most efficient among all baseline methods.

Removing reflection from a single image is challenging due to the absence of general reflection priors. Although existing methods incorporate extensive user guidance for satisfactory performance, they often lack the flexibility to adapt user guidance in different modalities, and dense user interactions further limit their practicality. To alleviate these problems, this paper presents FIRM, a novel framework for Flexible Interactive image Reflection reMoval with various forms of guidance, where users can provide sparse visual guidance (e.g., points, boxes, or strokes) or text descriptions for better reflection removal. Firstly, we design a novel user guidance conversion module (UGC) to transform different forms of guidance into unified contrastive masks. The contrastive masks provide explicit cues for identifying reflection and transmission layers in blended images. Secondly, we devise a contrastive mask-guided reflection removal network that comprises a newly proposed contrastive guidance interaction block (CGIB). This block leverages a unique cross-attention mechanism that merges contrastive masks with image features, allowing for precise layer separation. The proposed framework requires only 10% of the guidance time needed by previous interactive methods, which makes a step-change in flexibility. Extensive results on public real-world reflection removal datasets validate that our method demonstrates state-of-the-art reflection removal performance.

Generalizable active mapping in complex unknown environments remains a critical challenge for mobile robots. Existing methods, constrained by insufficient training data and conservative exploration strategies, exhibit limited generalizability across scenes with diverse layouts and complex connectivity. To enable scalable training and reliable evaluation, we introduce GLEAM-Bench, the first large-scale benchmark designed for generalizable active mapping with 1,152 diverse 3D scenes from synthetic and real-scan datasets. Building upon this foundation, we propose GLEAM, a unified generalizable exploration policy for active mapping. Its superior generalizability comes mainly from our semantic representations, long-term navigable goals, and randomized strategies. It significantly outperforms state-of-the-art methods, achieving 66.50% coverage (+9.49%) with efficient trajectories and improved mapping accuracy on 128 unseen complex scenes. Project page: https://xiao-chen.tech/gleam/.

While recent advances in neural radiance field enable realistic digitization for large-scale scenes, the image-capturing process is still time-consuming and labor-intensive. Previous works attempt to automate this process using the Next-Best-View (NBV) policy for active 3D reconstruction. However, the existing NBV policies heavily rely on handcrafted criteria, limited action space, or perscene optimized representations. These constraints limit their cross-dataset generalizability. To overcome them, we propose GenNBV, an end-to-end generalizable NBV policy. Our policy adopts a reinforcement learning (RL)-based framework and extends typical limited action space to 5D free space. It empowers our agent drone to scan from any viewpoint, and even interact with unseen geometries during training. To boost the cross-dataset generalizability, we also propose a novel multi-source state embedding, including geometric, semantic, and action representations. We establish a benchmark using the Isaac Gym simulator with the Houses3K and OmniObject3D datasets to evaluate this NBV policy. Experiments demonstrate that our policy achieves a 98.26% and 97.12% coverage ratio on unseen building-scale objects from these datasets, respectively, outperforming prior solutions.

In the realm of computer vision and robotics, embodied agents are expected to explore their environment and carry out human instructions. This necessitates the ability to fully understand 3D scenes given their first-person observations and contextualize them into language for interaction. However, traditional research focuses more on scene-level input and output setups from a global view. To address the gap, we introduce EmbodiedScan, a multi-modal, ego-centric 3D perception dataset and benchmark for holistic 3D scene understanding. It encompasses over 5k scans encapsulating 1M ego-centric RGB-D views, 1M language prompts, 160k 3D-oriented boxes spanning over 760 categories, some of which partially align with LVIS, and dense semantic occupancy with 80 common categories. Building upon this database, we introduce a baseline framework named Embodied Perceptron. It is capable of processing an arbitrary number of multi-modal inputs and demonstrates remarkable 3D perception capabilities, both within the two series of benchmarks we set up, i.e., fundamental 3D perception tasks and language-grounded tasks, and in the wild.

Recommender systems aim to provide personalized suggestions to users, helping them make effective decisions. However, recent evidence has revealed the untrustworthy aspects of advanced recommender systems, leading to harmful effects in safety-critical areas like finance and healthcare. This tutorial will offer a comprehensive overview of achieving trustworthy recommender systems. It will cover six important aspects: Safety & Robustness, Non-discrimination & Fairness, Explainability, Privacy, Environmental Well-being, and Accountability & Auditability. Each aspect will be defined and categorized, followed by a discussion of the latest research progress and notable works. Additionally, potential interactions among these aspects and future research directions for trustworthy recommender systems will be explored.

Pairwise learning strategies are prevalent for optimizing recommendation models on implicit feedback data, which usually learns user preference by discriminating between positive (i.e., clicked by a user) and negative items (i.e., obtained by negative sampling). However, the size of different item groups (specified by item attribute) is usually unevenly distributed. We empirically find that the commonly used uniform negative sampling strategy for pairwise algorithms (e.g., BPR) can inherit such data bias and oversample the majority item group as negative instances, severely countering group fairness on the item side. In this paper, we propose a Fairly adaptive Negative sampling approach (FairNeg), which improves item group fairness via adaptively adjusting the group-level negative sampling distribution in the training process. In particular, it first perceives the model’s unfairness status at each step and then adjusts the group-wise sampling distribution with an adaptive momentum update strategy for better facilitating fairness optimization. Moreover, a negative sampling distribution Mixup mechanism is proposed, which gracefully incorporates existing importance-aware sampling techniques intended for mining informative negative samples, thus allowing for achieving multiple optimization purposes. Extensive experiments on four public datasets show our proposed method’s superiority in group fairness enhancement and fairness-utility tradeoff.

Automatic liver lesion classification on computed tomography images is of great importance to early cancer diagnosis and remains a challenging task. State-of-the-art liver lesion classification algorithms are currently based on manually selected regions of interest (ROIs) or automatically detected ROIs. However, liver lesions usually vary in size and shape, which makes the ROI selection process labor-intensive and also poses an obstacle to automatic lesion detection. In this paper, we propose a dual-attention dilated residual network (DADRN) as a potential solution to lesion classification task without manual ROI selection or automatic lesion detection. We incorporated a novel dual-attention module in order to capture the non-local feature dependencies and help the deep neural network focus on the lesion area by enlarging the difference between the lesion area and nonlesion area. To the best of our knowledge, we are the first to employ the self-attention mechanism to address liver lesion classification task. In addition, the well-trained DADRN can be used for weakly-supervised lesion localization without any architectural change or retraining. Experiment results show that DADRN could achieve a lesion classification accuracy comparable to that of the state-of-the-art ROI-based method and outperformed state-of-the-art attention-based approaches in both liver lesion classification and localization tasks.

Convolutional neural networks (CNNs) have achieved great success in numerous challenging vision tasks, and have great potential for object detection in natural images. Compared with the natural images, medical images exhibit some unique characteristics. Therefore, substantial challenges still remain in this field. The first challenge is to develop a method for effectively distilling enhancement patterns from the dynamic CT images. Moreover, since tumor sizes vary greatly and small lesions are important for early liver tumor detection, lesion detection with a widely variable scale is another challenge. In this paper, we propose a multi-stream scale-insensitive convolutional and recurrent neural network (MSCR) for liver tumor detection. Specifically, we propose the use of grouped convolutional long short-term memory (GCLSTM) to extract enhancement patterns, which is developed as a plug-and-play module. Experiments show that the MSCR framework exhibits superior performance over state-of-the-art approaches, achieving an average precision of 77.06% for detection of focal liver lesions. We have released the code of MSCR in1.