Search Results for author:
Found 5 papers, 4 papers with code
VL-Mamba: Exploring State Space Models for Multimodal Learning
The extensive experiments on diverse multimodal benchmarks with competitive performance show the effectiveness of our proposed VL-Mamba and demonstrate the great potential of applying state space models for multimodal learning tasks.
Ranked #61 on
Visual Question Answering
on MM-Vet
GLOBER: Coherent Non-autoregressive Video Generation via GLOBal Guided Video DecodER
Specifically, we propose a video auto-encoder, where a video encoder encodes videos into global features, and a video decoder, built on a diffusion model, decodes the global features and synthesizes video frames in a non-autoregressive manner.
VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and Dataset
Based on the proposed VAST-27M dataset, we train an omni-modality video-text foundational model named VAST, which can perceive and process vision, audio, and subtitle modalities from video, and better support various tasks including vision-text, audio-text, and multi-modal video-text tasks (retrieval, captioning and QA).
Ranked #1 on
Image Captioning
on COCO Captions
(SPICE metric, using extra
training data)
MOSO: Decomposing MOtion, Scene and Object for Video Prediction
Experimental results demonstrate that our method achieves new state-of-the-art performance on five challenging benchmarks for video prediction and unconditional video generation: BAIR, RoboNet, KTH, KITTI and UCF101.
OPT: Omni-Perception Pre-Trainer for Cross-Modal Understanding and Generation
In this paper, we propose an Omni-perception Pre-Trainer (OPT) for cross-modal understanding and generation, by jointly modeling visual, text and audio resources.
Ranked #1 on
Image Retrieval
on Localized Narratives
