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Found 10 papers, 4 papers with code
Unimodal and Crossmodal Refinement Network for Multimodal Sequence Fusion
Specifically, to improve unimodal representations, a unimodal refinement module is designed to refine modality-specific learning via iteratively updating the distribution with transformer-based attention layers.
MACS: Multi-source Audio-to-image Generation with Contextual Significance and Semantic Alignment
In the second stage, efficient image generation is achieved by mapping the separated audio signals to the generation condition using only a trainable adapter and a MLP layer.
SparseMamba-PCL: Scribble-Supervised Medical Image Segmentation via SAM-Guided Progressive Collaborative Learning
Scribble annotations significantly reduce the cost and labor required for dense labeling in large medical datasets with complex anatomical structures.
Benchmarking Cross-Domain Audio-Visual Deception Detection
Automated deception detection is crucial for assisting humans in accurately assessing truthfulness and identifying deceptive behavior.
Improving Concept Alignment in Vision-Language Concept Bottleneck Models
To address this issue, we propose a novel Contrastive Semi-Supervised (CSS) learning method that leverages a few labeled concept samples to activate truthful visual concepts and improve concept alignment in the CLIP model.
MIMIC: Mask Image Pre-training with Mix Contrastive Fine-tuning for Facial Expression Recognition
In addition, when compared with the domain disparity existing between face datasets and FER datasets, the divergence between general datasets and FER datasets is more pronounced.
Retrieving Multimodal Information for Augmented Generation: A Survey
As Large Language Models (LLMs) become popular, there emerged an important trend of using multimodality to augment the LLMs' generation ability, which enables LLMs to better interact with the world.
Audio-Visual Deception Detection: DOLOS Dataset and Parameter-Efficient Crossmodal Learning
Despite this, deception detection research is hindered by the lack of high-quality deception datasets, as well as the difficulties of learning multimodal features effectively.
Flexible-modal Deception Detection with Audio-Visual Adapter
Detecting deception by human behaviors is vital in many fields such as custom security and multimedia anti-fraud.
Towards Photo-Realistic Virtual Try-On by Adaptively Generating$\leftrightarrow$Preserving Image Content
First, a semantic layout generation module utilizes semantic segmentation of the reference image to progressively predict the desired semantic layout after try-on.
Ranked #4 on
Virtual Try-on
on VITON
(IS metric)
