15 accepted papers
Large multimodal models (LMMs) have achieved impressive performance on various vision-language tasks, but their substantial computational and memory costs hinder their practical deployment. Existing compression methods often decouple low-rank decomposition and quantization, leading to compounded rec…
With the booming development of multimodal data (e.g., image, text) on internet platforms, multimodal sequential recommendation methods continue to emerge. Most existing methods incorporate item modal features as auxiliary information, typically concatenating them to learn unified user representatio
Multimodal large language models (MLLMs) have achieved remarkable success on diverse visual-language tasks. However, fixed-resolution models face challenges in perceiving fine-grained visual details, particularly due to *distracted attention* and *blurry vision*. To address these issues, we propose
We introduce the Probabilistic Coin Change Problem (PCCP), a novel variant of the classical Combination Coin Change Problem (CCCP), motivated by a real-world scientific inverse task. The goal of CCCP is to enumerate all unordered combinations of coin denominations that sum to a given target. In PCCP
Session-based recommendation (SBR) provides personalized recommendations by analyzing the interactions of anonymous session users. Recent approaches based on graph neural networks (GNNs) focus on pairwise relations to infer potential user preferences. However, real-world user interactions are often…
Visual Commonsense Reasoning (VCR) is a cognitive task, challenging models to answer visual questions, and to explain the rationale behind their answers. While Large Language Models (LLMs) offer potential for this task, VCR’s complex scenes require specialized approaches to activate their commonsens…
Federated Learning (FL) has become a prominent framework for maintaining privacy in recommender systems by enabling decentralized model training. Despite its benefits, traditional Federated Recommender Systems (FRSs)—often relying on collaborative filtering or generative models such as Variational A…
The multimodal recommendation has gradually become the infrastructure of online media platforms, enabling them to provide personalized service to users through a joint modeling of user historical behaviors (e.g., purchases, clicks) and item various modalities (e.g., visual and textual). The majority…
The lack of interaction data of new items in recommendation systems leads to the problem of cold-start item recommendations. Current methods usually approximate content features of the items to interaction embeddings and then use content features for prediction. However, these methods typically lear…
Federated multimedia recommendation remains unexplored due to the high dimensionality of multimedia context, which limits the federated optimization on resource-constrained user devices. To address this issue, we propose a resource-efficient and secure federated learning framework for multimedia rec…
Visual Question Answering (VQA) aims to answer the natural language question about a given image by understanding multimodal content. However, the answer quality of most existing visual-language pre-training (VLP) methods is still limited, mainly due to: (1) Incompatibility. Upstream pre-training ta…
Next-basket recommendation (NBR) is a type of recommendation task that focuses on mining user interests based on the sequential basket records in which users purchase multiple items at a time. Limited by the sparsity brought by short-term user interaction behavior, existing NBR methods typically fai…
Accurate fact verification depends on performing fine-grained reasoning over crucial entities by capturing their latent logical relations hidden in multiple evidence clues, which is generally lacking in existing fact verification models. In this work, we propose a novel Global-to-Local Aggregation a…
As a more natural and intelligent interaction manner, multimodal task-oriented dialog system recently has received great attention and many remarkable progresses have been achieved. Nevertheless, almost all existing studies follow the pipeline to first learn intra-modal features separately and then…
Recent years has witnessed the remarkable success in end-to-end task-oriented dialog system, especially when incorporating external knowledge information. However, the quality of most existing models’ generated response is still limited, mainly due to their lack of fine-grained reasoning on determin…