Explainable CTR Prediction via LLM Reasoning

Recommendation Systems have become integral to modern user experiences, but lack transparency in their decision-making processes. Existing explainable recommendation methods are hindered by reliance on a post-hoc paradigm, wherein explanation generators are trained independently of the underlying recommender models. This paradigm necessitates substantial human effort in data construction and raises concerns about explanation reliability. In this paper, we present ExpCTR, a novel framework that integrates large language model based explanation generation directly into the CTR prediction process. Inspired by recent advances in reinforcement learning, we employ two carefully designed reward mechanisms, LC alignment, which ensures explanations reflect user intentions, and IC alignment, which maintains consistency with traditional ID-based CTR models. Our approach incorporates an efficient training paradigm with LoRA and a three-stage iterative process. ExpCTR circumvents the need for extensive explanation datasets while fostering synergy between CTR prediction and explanation generation. Experimental results demonstrate that ExpCTR significantly enhances both recommendation accuracy and interpretability across three real-world datasets.

See What You Seek: Semantic Contextual Integration for Cloth-Changing Person Re-Identification

Cloth-changing person re-identification (CC-ReID) aims to match individuals across multiple surveillance cameras despite variations in clothing. Existing methods typically focus on mitigating the effects of clothing changes or enhancing ID-relevant features but often struggle to capture complex semantic information. In this paper, we propose a novel prompt learning framework, Semantic Contextual Integration (SCI), for CC-ReID, which leverages the visual-text representation capabilities of CLIP to minimize the impact of clothing changes and enhance ID-relevant features. Specifically, we introduce Semantic Separation Enhancement (SSE) module, which uses dual learnable text tokens to separately capture confounding and clothing-related semantic information, effectively isolating ID-relevant features from distracting clothing semantics. Additionally, we develop a Semantic-Guided Interaction Module (SIM) that uses orthogonalized text features to guide visual representations, sharpening the model's focus on distinctive ID characteristics. This integration enhances the model's discriminative power and enriches the visual context with high-dimensional semantic insights. Extensive experiments on three CC-ReID datasets demonstrate that our method outperforms state-of-the-art techniques. The code will be released at github.

Break the ID-Language Barrier: An Adaption Framework for Sequential Recommendation

The recent breakthrough of large language models (LLMs) in natural language processing has sparked exploration in recommendation systems, however, their limited domain-specific knowledge remains a critical bottleneck. Specifically, LLMs lack key pieces of information crucial for sequential recommendations, such as user behavior patterns. To address this critical gap, we propose IDLE-Adapter, a novel framework that integrates pre-trained ID embeddings, rich in domain-specific knowledge, into LLMs to improve recommendation accuracy. IDLE-Adapter acts as a bridge, transforming sparse user-item interaction data into dense, LLM-compatible representations through a Pre-trained ID Sequential Model, Dimensionality Alignment, Layer-wise Embedding Refinement, and Layer-wise Distribution Alignment. Furthermore, IDLE-Adapter demonstrates remarkable flexibility by seamlessly integrating ID embeddings from diverse ID-based sequential models and LLM architectures. Extensive experiments across various datasets demonstrate the superiority of IDLE-Adapter, achieving over 10\% and 20\% improvements in HitRate@5 and NDCG@5 metrics, respectively, compared to state-of-the-art methods.

Distribution alignment based transfer fusion frameworks on quantum devices for seeking quantum advantages

The scarcity of labelled data is specifically an urgent challenge in the field of quantum machine learning (QML). Two transfer fusion frameworks are proposed in this paper to predict the labels of a target domain data by aligning its distribution to a different but related labelled source domain on quantum devices. The frameworks fuses the quantum data from two different, but related domains through a quantum information infusion channel. The predicting tasks in the target domain can be achieved with quantum advantages by post-processing quantum measurement results. One framework, the quantum basic linear algebra subroutines (QBLAS) based implementation, can theoretically achieve the procedure of transfer fusion with quadratic speedup on a universal quantum computer. In addition, the other framework, a hardware-scalable architecture, is implemented on the noisy intermediate-scale quantum (NISQ) devices through a variational hybrid quantum-classical procedure. Numerical experiments on the synthetic and handwritten digits datasets demonstrate that the variatioinal transfer fusion (TF) framework can reach state-of-the-art (SOTA) quantum DA method performance.

Prompting Continual Person Search

The development of person search techniques has been greatly promoted in recent years for its superior practicality and challenging goals. Despite their significant progress, existing person search models still lack the ability to continually learn from increaseing real-world data and adaptively process input from different domains. To this end, this work introduces the continual person search task that sequentially learns on multiple domains and then performs person search on all seen domains. This requires balancing the stability and plasticity of the model to continually learn new knowledge without catastrophic forgetting. For this, we propose a Prompt-based Continual Person Search (PoPS) model in this paper. First, we design a compositional person search transformer to construct an effective pre-trained transformer without exhaustive pre-training from scratch on large-scale person search data. This serves as the fundamental for prompt-based continual learning. On top of that, we design a domain incremental prompt pool with a diverse attribute matching module. For each domain, we independently learn a set of prompts to encode the domain-oriented knowledge. Meanwhile, we jointly learn a group of diverse attribute projections and prototype embeddings to capture discriminative domain attributes. By matching an input image with the learned attributes across domains, the learned prompts can be properly selected for model inference. Extensive experiments are conducted to validate the proposed method for continual person search. The source code is available at https://github.com/PatrickZad/PoPS.

UGotMe: An Embodied System for Affective Human-Robot Interaction

Equipping humanoid robots with the capability to understand emotional states of human interactants and express emotions appropriately according to situations is essential for affective human-robot interaction. However, enabling current vision-aware multimodal emotion recognition models for affective human-robot interaction in the real-world raises embodiment challenges: addressing the environmental noise issue and meeting real-time requirements. First, in multiparty conversation scenarios, the noises inherited in the visual observation of the robot, which may come from either 1) distracting objects in the scene or 2) inactive speakers appearing in the field of view of the robot, hinder the models from extracting emotional cues from vision inputs. Secondly, realtime response, a desired feature for an interactive system, is also challenging to achieve. To tackle both challenges, we introduce an affective human-robot interaction system called UGotMe designed specifically for multiparty conversations. Two denoising strategies are proposed and incorporated into the system to solve the first issue. Specifically, to filter out distracting objects in the scene, we propose extracting face images of the speakers from the raw images and introduce a customized active face extraction strategy to rule out inactive speakers. As for the second issue, we employ efficient data transmission from the robot to the local server to improve realtime response capability. We deploy UGotMe on a human robot named Ameca to validate its real-time inference capabilities in practical scenarios. Videos demonstrating real-world deployment are available at https://pi3-141592653.github.io/UGotMe/.

EIANet: A Novel Domain Adaptation Approach to Maximize Class Distinction with Neural Collapse Principles

Source-free domain adaptation (SFDA) aims to transfer knowledge from a labelled source domain to an unlabelled target domain. A major challenge in SFDA is deriving accurate categorical information for the target domain, especially when sample embeddings from different classes appear similar. This issue is particularly pronounced in fine-grained visual categorization tasks, where inter-class differences are subtle. To overcome this challenge, we introduce a novel ETF-Informed Attention Network (EIANet) to separate class prototypes by utilizing attention and neural collapse principles. More specifically, EIANet employs a simplex Equiangular Tight Frame (ETF) classifier in conjunction with an attention mechanism, facilitating the model to focus on discriminative features and ensuring maximum class prototype separation. This innovative approach effectively enlarges the feature difference between different classes in the latent space by locating salient regions, thereby preventing the misclassification of similar but distinct category samples and providing more accurate categorical information to guide the fine-tuning process on the target domain. Experimental results across four SFDA datasets validate EIANet's state-of-the-art performance. Code is available at: https://github.com/zichengpan/EIANet.

SpectralKAN: Kolmogorov-Arnold Network for Hyperspectral Images Change Detection

It has been verified that deep learning methods, including convolutional neural networks (CNNs), graph neural networks (GNNs), and transformers, can accurately extract features from hyperspectral images (HSIs). These algorithms perform exceptionally well on HSIs change detection (HSIs-CD). However, the downside of these impressive results is the enormous number of parameters, FLOPs, GPU memory, training and test times required. In this paper, we propose an spectral Kolmogorov-Arnold Network for HSIs-CD (SpectralKAN). SpectralKAN represent a multivariate continuous function with a composition of activation functions to extract HSIs feature and classification. These activation functions are b-spline functions with different parameters that can simulate various functions. In SpectralKAN, a KAN encoder is proposed to enhance computational efficiency for HSIs. And a spatial-spectral KAN encoder is introduced, where the spatial KAN encoder extracts spatial features and compresses the spatial dimensions from patch size to one. The spectral KAN encoder then extracts spectral features and classifies them into changed and unchanged categories. We use five HSIs-CD datasets to verify the effectiveness of SpectralKAN. Experimental verification has shown that SpectralKAN maintains high HSIs-CD accuracy while requiring fewer parameters, FLOPs, GPU memory, training and testing times, thereby increasing the efficiency of HSIs-CD. The code will be available at https://github.com/yanhengwang-heu/SpectralKAN.

CoR-GS: Sparse-View 3D Gaussian Splatting via Co-Regularization

3D Gaussian Splatting (3DGS) creates a radiance field consisting of 3D Gaussians to represent a scene. With sparse training views, 3DGS easily suffers from overfitting, negatively impacting the reconstruction quality. This paper introduces a new co-regularization perspective for improving sparse-view 3DGS. When training two 3D Gaussian radiance fields with the same sparse views of a scene, we observe that the two radiance fields exhibit \textit{point disagreement} and \textit{rendering disagreement} that can unsupervisedly predict reconstruction quality, stemming from the sampling implementation in densification. We further quantify the point disagreement and rendering disagreement by evaluating the registration between Gaussians' point representations and calculating differences in their rendered pixels. The empirical study demonstrates the negative correlation between the two disagreements and accurate reconstruction, which allows us to identify inaccurate reconstruction without accessing ground-truth information. Based on the study, we propose CoR-GS, which identifies and suppresses inaccurate reconstruction based on the two disagreements: (\romannumeral1) Co-pruning considers Gaussians that exhibit high point disagreement in inaccurate positions and prunes them. (\romannumeral2) Pseudo-view co-regularization considers pixels that exhibit high rendering disagreement are inaccurately rendered and suppress the disagreement. Results on LLFF, Mip-NeRF360, DTU, and Blender demonstrate that CoR-GS effectively regularizes the scene geometry, reconstructs the compact representations, and achieves state-of-the-art novel view synthesis quality under sparse training views.

Robust Synthetic-to-Real Transfer for Stereo Matching

With advancements in domain generalized stereo matching networks, models pre-trained on synthetic data demonstrate strong robustness to unseen domains. However, few studies have investigated the robustness after fine-tuning them in real-world scenarios, during which the domain generalization ability can be seriously degraded. In this paper, we explore fine-tuning stereo matching networks without compromising their robustness to unseen domains. Our motivation stems from comparing Ground Truth (GT) versus Pseudo Label (PL) for fine-tuning: GT degrades, but PL preserves the domain generalization ability. Empirically, we find the difference between GT and PL implies valuable information that can regularize networks during fine-tuning. We also propose a framework to utilize this difference for fine-tuning, consisting of a frozen Teacher, an exponential moving average (EMA) Teacher, and a Student network. The core idea is to utilize the EMA Teacher to measure what the Student has learned and dynamically improve GT and PL for fine-tuning. We integrate our framework with state-of-the-art networks and evaluate its effectiveness on several real-world datasets. Extensive experiments show that our method effectively preserves the domain generalization ability during fine-tuning.