342 citations · 767 across the 21 of their papers we have counts for
21 papers
AssistGPT: A General Multi-modal Assistant that can Plan, Execute, Inspect, and Learn
Difei Gao, Lei Ji, Luowei Zhou +4
Recent research on Large Language Models (LLMs) has led to remarkable advancements in general NLP AI assistants. Some studies have further explored the use of LLMs for planning and…
MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks
Weicheng Kuo, AJ Piergiovanni, Dahun Kim +9
The development of language models have moved from encoder-decoder to decoder-only designs. In addition, we observe that the two most popular multimodal tasks, the generative and c…
MIST: Multi-modal Iterative Spatial-Temporal Transformer for Long-form Video Question Answering
Difei Gao, Luowei Zhou, Lei Ji +3
To build Video Question Answering (VideoQA) systems capable of assisting humans in daily activities, seeking answers from long-form videos with diverse and complex events is a must…
OmniVL:One Foundation Model for Image-Language and Video-Language Tasks
Junke Wang, Dongdong Chen, Zuxuan Wu +7
This paper presents OmniVL, a new foundation model to support both image-language and video-language tasks using one universal architecture. It adopts a unified transformer-based v…
Video Mobile-Former: Video Recognition with Efficient Global Spatial-temporal Modeling
Rui Wang, Zuxuan Wu, Dongdong Chen +6
Transformer-based models have achieved top performance on major video recognition benchmarks. Benefiting from the self-attention mechanism, these models show stronger ability of mo…
Learning Visual Representation from Modality-Shared Contrastive Language-Image Pre-training
Haoxuan You, Luowei Zhou, Bin Xiao +5
Large-scale multi-modal contrastive pre-training has demonstrated great utility to learn transferable features for a range of downstream tasks by mapping multiple modalities into a…
Visual Clues: Bridging Vision and Language Foundations for Image Paragraph Captioning
Yujia Xie, Luowei Zhou, Xiyang Dai +4
People say, "A picture is worth a thousand words". Then how can we get the rich information out of the image? We argue that by using visual clues to bridge large pretrained vision…
Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners
Zhenhailong Wang, Manling Li, Ruochen Xu +10
The goal of this work is to build flexible video-language models that can generalize to various video-to-text tasks from few examples, such as domain-specific captioning, question…
Multimodal Adaptive Distillation for Leveraging Unimodal Encoders for Vision-Language Tasks
Zhecan Wang, Noel Codella, Yen-Chun Chen +8
Cross-modal encoders for vision-language (VL) tasks are often pretrained with carefully curated vision-language datasets. While these datasets reach an order of 10 million samples,…
CLIP-TD: CLIP Targeted Distillation for Vision-Language Tasks
Zhecan Wang, Noel Codella, Yen-Chun Chen +7
Contrastive language-image pretraining (CLIP) links vision and language modalities into a unified embedding space, yielding the tremendous potential for vision-language (VL) tasks.…
CLIP-Event: Connecting Text and Images with Event Structures
Manling Li, Ruochen Xu, Shuohang Wang +6
Vision-language (V+L) pretraining models have achieved great success in supporting multimedia applications by understanding the alignments between images and text. While existing v…
RegionCLIP: Region-based Language-Image Pretraining
Yiwu Zhong, Jianwei Yang, Pengchuan Zhang +8
Contrastive language-image pretraining (CLIP) using image-text pairs has achieved impressive results on image classification in both zero-shot and transfer learning settings. Howev…
BEVT: BERT Pretraining of Video Transformers
Rui Wang, Dongdong Chen, Zuxuan Wu +6
This paper studies the BERT pretraining of video transformers. It is a straightforward but worth-studying extension given the recent success from BERT pretraining of image transfor…
Florence: A New Foundation Model for Computer Vision
Lu Yuan, Dongdong Chen, Yi-Ling Chen +20
Automated visual understanding of our diverse and open world demands computer vision models to generalize well with minimal customization for specific tasks, similar to human visio…
VALUE: A Multi-Task Benchmark for Video-and-Language Understanding Evaluation
Linjie Li, Jie Lei, Zhe Gan +12
Most existing video-and-language (VidL) research focuses on a single dataset, or multiple datasets of a single task. In reality, a truly useful VidL system is expected to be easily…
CUPID: Adaptive Curation of Pre-training Data for Video-and-Language Representation Learning
Luowei Zhou, Jingjing Liu, Yu Cheng +2
This work concerns video-language pre-training and representation learning. In this now ubiquitous training scheme, a model first performs pre-training on paired videos and text (e…
UC2: Universal Cross-lingual Cross-modal Vision-and-Language Pre-training
Mingyang Zhou, Luowei Zhou, Shuohang Wang +4
Vision-and-language pre-training has achieved impressive success in learning multimodal representations between vision and language. To generalize this success to non-English langu…
Less is More: ClipBERT for Video-and-Language Learning via Sparse Sampling
Jie Lei, Linjie Li, Luowei Zhou +4
The canonical approach to video-and-language learning (e.g., video question answering) dictates a neural model to learn from offline-extracted dense video features from vision mode…
Temporally Guided Articulated Hand Pose Tracking in Surgical Videos
Nathan Louis, Luowei Zhou, Steven J. Yule +5
Articulated hand pose tracking is an under-explored problem that carries the potential for use in an extensive number of applications, especially in the medical domain. With a robu…
Cluster-Former: Clustering-based Sparse Transformer for Long-Range Dependency Encoding
Shuohang Wang, Luowei Zhou, Zhe Gan +5
Transformer has become ubiquitous in the deep learning field. One of the key ingredients that destined its success is the self-attention mechanism, which allows fully-connected con…
Unified Vision-Language Pre-Training for Image Captioning and VQA
Luowei Zhou, Hamid Palangi, Lei Zhang +3
This paper presents a unified Vision-Language Pre-training (VLP) model. The model is unified in that (1) it can be fine-tuned for either vision-language generation (e.g., image cap…