6 papers
AICA-Bench: Holistically Examining the Capabilities of VLMs in Affective Image Content Analysis
Dong She, Xianrong Yao, Liqun Chen +3
Vision-Language Models (VLMs) have demonstrated strong capabilities in perception, yet holistic Affective Image Content Analysis (AICA), which integrates perception, reasoning, and…
Sequences as Nodes for Contrastive Multimodal Graph Recommendation
Bucher Sahyouni, Matthew Vowels, Liqun Chen +1
To tackle cold-start and data sparsity issues in recommender systems, numerous multimodal, sequential, and contrastive techniques have been proposed. While these augmentations can…
Multimodal Enhancement of Sequential Recommendation
Bucher Sahyouni, Matthew Vowels, Liqun Chen +1
We propose a novel recommender framework, MuSTRec (Multimodal and Sequential Transformer-based Recommendation), that unifies multimodal and sequential recommendation paradigms. MuS…
DSL: Understanding and Improving Softmax Recommender Systems with Competition-Aware Scaling
Bucher Sahyouni, Matthew Vowels, Liqun Chen +1
Softmax Loss (SL) is being increasingly adopted for recommender systems (RS) as it has demonstrated better performance, robustness and fairness. Yet in implicit-feedback, a single…
Melodia: Training-Free Music Editing Guided by Attention Probing in Diffusion Models
Yi Yang, Haowen Li, Tianxiang Li +4
Text-to-music generation technology is progressing rapidly, creating new opportunities for musical composition and editing. However, existing music editing methods often fail to pr…
Differential Adjusted Parity for Learning Fair Representations
Bucher Sahyouni, Matthew Vowels, Liqun Chen +1
The development of fair and unbiased machine learning models remains an ongoing objective for researchers in the field of artificial intelligence. We introduce the Differential Adj…