Search Results for author:
Found 29 papers, 17 papers with code
Revolutionizing Text-to-Image Retrieval as Autoregressive Token-to-Voken Generation
Despite its great potential, existing generative approaches are limited due to the following issues: insufficient visual information in identifiers, misalignment with high-level semantics, and learning gap towards the retrieval target.
Harnessing Large Language Models for Multimodal Product Bundling
Recent methods utilize multimodal information through sophisticated extractors for bundling, but remain limited by inferior semantic understanding, the restricted scope of knowledge, and an inability to handle cold-start issues.
PromptDSI: Prompt-based Rehearsal-free Instance-wise Incremental Learning for Document Retrieval
Differentiable Search Index (DSI) utilizes Pre-trained Language Models (PLMs) for efficient document retrieval without relying on external indexes.
MMGRec: Multimodal Generative Recommendation with Transformer Model
Multimodal recommendation aims to recommend user-preferred candidates based on her/his historically interacted items and associated multimodal information.
Double Mixture: Towards Continual Event Detection from Speech
To address the challenges of catastrophic forgetting and effective disentanglement, we propose a novel method, 'Double Mixture.'
Data-efficient Fine-tuning for LLM-based Recommendation
To pursue the two objectives, we propose a novel data pruning method based on two scores, i. e., influence score and effort score, to efficiently identify the influential samples.
Instilling Multi-round Thinking to Text-guided Image Generation
This paper delves into the text-guided image editing task, focusing on modifying a reference image according to user-specified textual feedback to embody specific attributes.
Attribute-driven Disentangled Representation Learning for Multimodal Recommendation
To obtain robust and independent representations for each factor associated with a specific attribute, we first disentangle the representations of features both within and across different modalities.
MultiCBR: Multi-view Contrastive Learning for Bundle Recommendation
It does, however, have two limitations: 1) the two-view formulation does not fully exploit all the heterogeneous relations among users, bundles and items; and 2) the "early contrast and late fusion" framework is less effective in capturing user preference and difficult to generalize to multiple views.
Leveraging Multimodal Features and Item-level User Feedback for Bundle Construction
Specifically, we use self-attention modules to combine the multimodal and multi-item features, and then leverage both item- and bundle-level contrastive learning to enhance the representation learning, thus to counter the modality missing, noise, and sparsity problems.
Federated Class-Incremental Learning with Prompting
For FCIL, the local and global models may suffer from catastrophic forgetting on old classes caused by the arrival of new classes and the data distributions of clients are non-independent and identically distributed (non-iid).
Online Distillation-enhanced Multi-modal Transformer for Sequential Recommendation
To be specific, we first introduce an ID-aware Multi-modal Transformer module in the item representation learning stage to facilitate information interaction among different features.
StyleEDL: Style-Guided High-order Attention Network for Image Emotion Distribution Learning
Emotion distribution learning has gained increasing attention with the tendency to express emotions through images.
General Debiasing for Multimodal Sentiment Analysis
To this end, we propose a general debiasing framework based on Inverse Probability Weighting (IPW), which adaptively assigns small weights to the samples with larger bias (i. e., the severer spurious correlations).
LightGT: A Light Graph Transformer for Multimedia Recommendation
Considering its challenges in effectiveness and efficiency, we propose a novel Transformer-based recommendation model, termed as Light Graph Transformer model (LightGT).
Ranked #1 on
Multi-Media Recommendation
on Kwai
(Recall@10 metric)
Revisiting Conversation Discourse for Dialogue Disentanglement
Conversation utterances are essentially organized and described by the underlying discourse, and thus dialogue disentanglement requires the full understanding and harnessing of the intrinsic discourse attribute.
Dual Semantic Knowledge Composed Multimodal Dialog Systems
Thereafter, considering that the attribute knowledge and relation knowledge can benefit the responding to different levels of questions, we design a multi-level knowledge composition module in MDS-S2 to obtain the latent composed response representation.
Micro-video Tagging via Jointly Modeling Social Influence and Tag Relation
Existing methods mostly focus on analyzing video content, neglecting users' social influence and tag relation.
HS-GCN: Hamming Spatial Graph Convolutional Networks for Recommendation
Towards this end, existing methods tend to code users by modeling their Hamming similarities with the items they historically interact with, which are termed as the first-order similarities in this work.
MRTNet: Multi-Resolution Temporal Network for Video Sentence Grounding
In this work, we propose a novel multi-resolution temporal video sentence grounding network: MRTNet, which consists of a multi-modal feature encoder, a Multi-Resolution Temporal (MRT) module, and a predictor module.
Multi-queue Momentum Contrast for Microvideo-Product Retrieval
The booming development and huge market of micro-videos bring new e-commerce channels for merchants.
Causal Inference for Knowledge Graph based Recommendation
Knowledge Graph (KG), as a side-information, tends to be utilized to supplement the collaborative filtering (CF) based recommendation model.
Privacy-Preserving Synthetic Data Generation for Recommendation Systems
At the item level, a synthetic data generation module is proposed to generate a synthetic item corresponding to the selected item based on the user's preferences.
Semantic-aware Modular Capsule Routing for Visual Question Answering
Visual Question Answering (VQA) is fundamentally compositional in nature, and many questions are simply answered by decomposing them into modular sub-problems.
Joint Answering and Explanation for Visual Commonsense Reasoning
Given that our framework is model-agnostic, we apply it to the existing popular baselines and validate its effectiveness on the benchmark dataset.
Dual Preference Distribution Learning for Item Recommendation
Moreover, we can summarize a preferred attribute profile for each user, depicting his/her preferred item attributes.
Contrastive Learning for Cold-Start Recommendation
It aims to maximize the mutual dependencies between item content and collaborative signals.
MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video
Existing works on multimedia recommendation largely exploit multi-modal contents to enrich item representations, while less effort is made to leverage information interchange between users and items to enhance user representations and further capture user's fine-grained preferences on different modalities.
Ranked #1 on
Multi-Media Recommendation
on MovieLens 10M
Personalized Hashtag Recommendation for Micro-videos
The hashtags, that a user provides to a post (e. g., a micro-video), are the ones which in her mind can well describe the post content where she is interested in.
