Open-Vocabulary Multi-Label Recognition (OV-MLR) aims to identify multiple seen and unseen object categories within an image, requiring both precise intra-class localization to pinpoint objects and effective inter-class reasoning to model complex category dependencies. While Vision-Language Pre-training (VLP) models offer a strong open-vocabulary foundation, they often struggle with fine-grained localization under weak supervision and typically fail to explicitly leverage structured relational knowledge beyond basic semantics, limiting performance especially for unseen classes. To overcome these limitations, we propose the Dual Adaptive Refinement Transfer (DART) framework. DART enhances a frozen VLP backbone via two synergistic adaptive modules. For intra-class refinement, an Adaptive Refinement Module (ARM) refines patch features adaptively, coupled with a novel Weakly Supervised Patch Selecting (WPS) loss that enables discriminative localization using only image-level labels. Concurrently, for inter-class transfer, an Adaptive Transfer Module (ATM) leverages a Class Relationship Graph (CRG), constructed using structured knowledge mined from a Large Language Model (LLM), and employs graph attention network to adaptively transfer relational information between class representations. DART is the first framework, to our knowledge, to explicitly integrate external LLM-derived relational knowledge for adaptive inter-class transfer while simultaneously performing adaptive intra-class refinement under weak supervision for OV-MLR. Extensive experiments on challenging benchmarks demonstrate that our DART achieves new state-of-the-art performance, validating its effectiveness.

Augmented reality and robotic navigation increasingly demand accurate and complete 3D scene reconstruction from egocentric viewpoints. However, dynamic occlusions—such as those caused by hand–object interactions—and frequent viewpoint changes often lead to persistent geometric incompleteness, hindering practical deployment. We present a diffusion-guided reconstruction framework that integrates 3D Gaussian Splatting (3DGS) with generative inpainting to achieve occlusion-free scene modeling. The method follows a two-stage pipeline: (1) Mask-guided Gaussian initialization constructs an occlusion-aware scene representation by explicitly excluding segmented occlusion regions; (2) Depth-aware diffusion refinement recovers missing structures through iterative cross-modal fusion of multi-view depth cues and pretrained semantic priors. This approach significantly improves scene completeness and visual fidelity over state-of-the-art methods. Extensive experiments demonstrate its robustness in occlusion-dense scenarios, especially in handling complex hand-induced occlusions, offering a practical solution for immersive augmented reality and robotic perception.

Egocentric Referring Video Object Segmentation (Ego-RVOS) aims to segment the specific object actively involved in a human action, as described by a language query, within first-person videos. This task is critical for understanding egocentric human behavior. However, achieving such segmentation robustly is challenging due to ambiguities inherent in egocentric videos and biases present in training data. Consequently, existing methods often struggle, learning spurious correlations from skewed object-action pairings in datasets and fundamental visual confounding factors of the egocentric perspective, such as rapid motion and frequent occlusions. To address these limitations, we introduce Causal Ego-REferring Segmentation (CERES), a plug-in causal framework that adapts strong, pre-trained RVOS backbones to the egocentric domain. CERES implements dual-modal causal intervention: applying backdoor adjustment principles to counteract language representation biases learned from dataset statistics, and leveraging front-door adjustment concepts to address visual confounding by intelligently integrating semantic visual features with geometric depth information guided by causal principles, creating representations more robust to egocentric distortions. Extensive experiments demonstrate that CERES achieves state-of-the-art performance on Ego-RVOS benchmarks, highlighting the potential of applying causal reasoning to build more reliable models for broader egocentric video understanding.

We study the helpful product reviews identification problem in this paper.We observe that the evidence-conclusion discourse relations, also known as arguments, often appear in product reviews, and we hypothesise that some argument-based features, e.g. the percentage of argumentative sentences, the evidencesconclusions ratios, are good indicators of helpful reviews.To validate this hypothesis, we manually annotate arguments in 110 hotel reviews, and investigate the effectiveness of several combinations of argument-based features.Experiments suggest that, when being used together with the argument-based features, the state-of-the-art baseline features can enjoy a performance boost (in terms of F1) of 11.01% in average.

Argumentation mining aims at automatically extracting the premises-claim discourse structures in natural language texts. There is a great demand for argumentation corpora for customer reviews. However, due to the controversial nature of the argumentation annotation task, there exist very few large-scale argumentation corpora for customer reviews. In this work, we novelly use the crowdsourcing technique to collect argumentation annotations in Chinese hotel reviews. As the first Chinese argumentation dataset, our corpus includes 4814 argument component annotations and 411 argument relation annotations, and its annotations qualities are comparable to some widely used argumentation corpora in other languages.

Argument Component Boundary Detection (ACBD) is an important sub-task in argumentation mining; it aims at identifying the word sequences that constitute argument components, and is usually considered as the first sub-task in the argumentation mining pipeline. Existing ACBD methods heavily depend on task-specific knowledge, and require considerable human efforts on feature-engineering. To tackle these problems, in this work, we formulate ACBD as a sequence labeling problem and propose a variety of Recurrent Neural Network (RNN) based methods, which do not use domain specific or handcrafted features beyond the relative position of the sentence in the document. In particular, we propose a novel joint RNN model that can predict whether sentences are argumentative or not, and use the predicted results to more precisely detect the argument component boundaries. We evaluate our techniques on two corpora from two different genres; results suggest that our joint RNN model obtain the state-of-the-art performance on both datasets.

In this letter, we jointly design relay antenna tilting with user scheduling so as to enhance the sum rate performance of a two-hop relay system, where the relay is equipped with a large-scale antenna array and performs zero-forcing processing. Building the fundamental of the joint design, a tight and tractable sum rate approximation is first derived by employing random matrix theory. Then the relay antenna downtilt and the number of active user pairs are jointly optimized to maximize the approximate sum rate. It is noted that the proposed scheme is independent of instantaneous channel state information. Therefore, it enjoys very low implementation complexity while improving the system performance.

We propose a low-complexity user selection scheme with zero-forcing precoding in multiuser MIMO downlink systems, where the base station (BS) is equipped with large-scale antenna arrays and the number of candidate-users is relatively small. The BS obtains the channel state information (CSI) of the user equipments (UEs) through the pilot-based minimum mean-square error channel estimation. Taking both the channel propagation and the UE location distribution into consideration, we first derive a deterministic approximation of the ergodic sum rate and investigate the optimal number of active UEs, K*, in the sense of sum rate maximization. Then, K* UEs are selected for simultaneous data transmission according to their large-scale channel fading. Small-scale channel fading is not taken into account in the selection procedure, thus reducing the computational complexity dramatically as well as improving the robustness of the proposed scheme in practice. Numerical simulations suggest that whether perfect CSI is available at the BS, our proposed scheme achieves high sum rate performance with very low complexity.