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Found 13 papers, 4 papers with code
Robust Audio-Visual Segmentation via Audio-Guided Visual Convergent Alignment
Accurately localizing audible objects based on audio-visual cues is the core objective of audio-visual segmentation.
Dynamic Derivation and Elimination: Audio Visual Segmentation with Enhanced Audio Semantics
Considering that not all derived audio representations directly correspond to visual features (e. g., off-screen sounds), we propose a dynamic elimination module to filter out non-matching elements.
JEN-1 DreamStyler: Customized Musical Concept Learning via Pivotal Parameters Tuning
In this paper, we propose a novel method for customized text-to-music generation, which can capture the concept from a two-minute reference music and generate a new piece of music conforming to the concept.
JEN-1 Composer: A Unified Framework for High-Fidelity Multi-Track Music Generation
With rapid advances in generative artificial intelligence, the text-to-music synthesis task has emerged as a promising direction for music generation.
BAVS: Bootstrapping Audio-Visual Segmentation by Integrating Foundation Knowledge
In a nutshell, our BAVS is designed to eliminate the interference of background noise or off-screen sounds in segmentation by establishing the audio-visual correspondences in an explicit manner.
JEN-1: Text-Guided Universal Music Generation with Omnidirectional Diffusion Models
Despite the task's significance, prevailing generative models exhibit limitations in music quality, computational efficiency, and generalization.
Ranked #5 on
Text-to-Music Generation
on MusicCaps
Audio-Visual Segmentation by Exploring Cross-Modal Mutual Semantics
However, we observed that prior arts are prone to segment a certain salient object in a video regardless of the audio information.
Dynamic Gradient Reactivation for Backward Compatible Person Re-identification
To address this issue, we propose the Ranking-based Backward Compatible Learning (RBCL), which directly optimizes the ranking metric between new features and old features.
M6-Fashion: High-Fidelity Multi-modal Image Generation and Editing
The fashion industry has diverse applications in multi-modal image generation and editing.
In-N-Out Generative Learning for Dense Unsupervised Video Segmentation
By contrast, pixel-level optimization is more explicit, however, it is sensitive to the visual quality of training data and is not robust to object deformation.
Super-Resolving Cross-Domain Face Miniatures by Peeking at One-Shot Exemplar
By iteratively updating the latent representations and our decoder, our DAP-FSR will be adapted to the target domain, thus achieving authentic and high-quality upsampled HR faces.
Consistent Structural Relation Learning for Zero-Shot Segmentation
Concretely, by exploring the pair-wise and list-wise structures, we impose the relations of generated visual features to be consistent with their counterparts in the semantic word embedding space.
Self-Correction for Human Parsing
To tackle the problem of learning with label noises, this work introduces a purification strategy, called Self-Correction for Human Parsing (SCHP), to progressively promote the reliability of the supervised labels as well as the learned models.
Ranked #2 on
Human Part Segmentation
on PASCAL-Part
