Search Results for author:
Found 12 papers, 10 papers with code
SongComposer: A Large Language Model for Lyric and Melody Composition in Song Generation
We present SongComposer, an innovative LLM designed for song composition.
Betrayed by Attention: A Simple yet Effective Approach for Self-supervised Video Object Segmentation
In this paper, we propose a simple yet effective approach for self-supervised video object segmentation (VOS).
InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and Composition
We propose InternLM-XComposer, a vision-language large model that enables advanced image-text comprehension and composition.
Ranked #9 on
Visual Question Answering (VQA)
on InfiMM-Eval
Semantics Meets Temporal Correspondence: Self-supervised Object-centric Learning in Videos
In the second stage, for each semantics, we randomly sample slots from the corresponding Gaussian distribution and perform masked feature aggregation within the semantic area to exploit temporal correspondence patterns for instance identification.
Prune Spatio-temporal Tokens by Semantic-aware Temporal Accumulation
Based on the STA score, we are able to progressively prune the tokens without introducing any additional parameters or requiring further re-training.
Motion-inductive Self-supervised Object Discovery in Videos
In this paper, we consider the task of unsupervised object discovery in videos.
Ranked #3 on
Unsupervised Object Segmentation
on DAVIS 2016
Static and Dynamic Concepts for Self-supervised Video Representation Learning
In this paper, we propose a novel learning scheme for self-supervised video representation learning.
Dual Contrastive Learning for Spatio-temporal Representation
In this way, the static scene and the dynamic motion are simultaneously encoded into the compact RGB representation.
Masked Autoencoders are Robust Data Augmentors
Specifically, MRA consistently enhances the performance on supervised, semi-supervised as well as few-shot classification.
Motion-aware Contrastive Video Representation Learning via Foreground-background Merging
To alleviate such bias, we propose \textbf{F}oreground-b\textbf{a}ckground \textbf{Me}rging (FAME) to deliberately compose the moving foreground region of the selected video onto the static background of others.
Enhancing Self-supervised Video Representation Learning via Multi-level Feature Optimization
The crux of self-supervised video representation learning is to build general features from unlabeled videos.
Towards More Practical Adversarial Attacks on Graph Neural Networks
Our theoretical and empirical analyses suggest that there is a discrepancy between the loss and mis-classification rate, as the latter presents a diminishing-return pattern when the number of attacked nodes increases.
