126 citations · 398 across the 28 of their papers we have counts for
28 papers
Video-Bench: A Comprehensive Benchmark and Toolkit for Evaluating Video-based Large Language Models
Munan Ning, Bin Zhu, Yujia Xie +5
Video-based large language models (Video-LLMs) have been recently introduced, targeting both fundamental improvements in perception and comprehension, and a diverse range of user i…
Album Storytelling with Iterative Story-aware Captioning and Large Language Models
Munan Ning, Yujia Xie, Dongdong Chen +5
This work studies how to transform an album to vivid and coherent stories, a task we refer to as "album storytelling". While this task can help preserve memories and facilitate exp…
i-Code Studio: A Configurable and Composable Framework for Integrative AI
Yuwei Fang, Mahmoud Khademi, Chenguang Zhu +8
Artificial General Intelligence (AGI) requires comprehensive understanding and generation capabilities for a variety of tasks spanning different modalities and functionalities. Int…
i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data
Ziyi Yang, Mahmoud Khademi, Yichong Xu +16
The convergence of text, visual, and audio data is a key step towards human-like artificial intelligence, however the current Vision-Language-Speech landscape is dominated by encod…
Image as First-Order Norm+Linear Autoregression: Unveiling Mathematical Invariance
Yinpeng Chen, Xiyang Dai, Dongdong Chen +4
This paper introduces a novel mathematical property applicable to diverse images, referred to as FINOLA (First-Order Norm+Linear Autoregressive). FINOLA represents each image in th…
ChatVideo: A Tracklet-centric Multimodal and Versatile Video Understanding System
Junke Wang, Dongdong Chen, Chong Luo +4
Existing deep video models are limited by specific tasks, fixed input-output spaces, and poor generalization capabilities, making it difficult to deploy them in real-world scenario…
OmniTracker: Unifying Object Tracking by Tracking-with-Detection
Junke Wang, Zuxuan Wu, Dongdong Chen +4
Visual Object Tracking (VOT) aims to estimate the positions of target objects in a video sequence, which is an important vision task with various real-world applications. Depending…
Layer Grafted Pre-training: Bridging Contrastive Learning And Masked Image Modeling For Label-Efficient Representations
Ziyu Jiang, Yinpeng Chen, Mengchen Liu +5
Recently, both Contrastive Learning (CL) and Mask Image Modeling (MIM) demonstrate that self-supervision is powerful to learn good representations. However, naively combining them…
Generalized Decoding for Pixel, Image, and Language
Xueyan Zou, Zi-Yi Dou, Jianwei Yang +11
We present X-Decoder, a generalized decoding model that can predict pixel-level segmentation and language tokens seamlessly. X-Decodert takes as input two types of queries: (i) gen…
Look Before You Match: Instance Understanding Matters in Video Object Segmentation
Junke Wang, Dongdong Chen, Zuxuan Wu +7
Exploring dense matching between the current frame and past frames for long-range context modeling, memory-based methods have demonstrated impressive results in video object segmen…
Masked Video Distillation: Rethinking Masked Feature Modeling for Self-supervised Video Representation Learning
Rui Wang, Dongdong Chen, Zuxuan Wu +5
Benefiting from masked visual modeling, self-supervised video representation learning has achieved remarkable progress. However, existing methods focus on learning representations…
Self-Supervised Learning based on Heat Equation
Yinpeng Chen, Xiyang Dai, Dongdong Chen +4
This paper presents a new perspective of self-supervised learning based on extending heat equation into high dimensional feature space. In particular, we remove time dependence by…
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…
TinyViT: Fast Pretraining Distillation for Small Vision Transformers
Kan Wu, Jinnian Zhang, Houwen Peng +4
Vision transformer (ViT) recently has drawn great attention in computer vision due to its remarkable model capability. However, most prevailing ViT models suffer from huge number o…
Should All Proposals be Treated Equally in Object Detection?
Yunsheng Li, Yinpeng Chen, Xiyang Dai +7
The complexity-precision trade-off of an object detector is a critical problem for resource constrained vision tasks. Previous works have emphasized detectors implemented with effi…
GLIPv2: Unifying Localization and Vision-Language Understanding
Haotian Zhang, Pengchuan Zhang, Xiaowei Hu +7
We present GLIPv2, a grounded VL understanding model, that serves both localization tasks (e.g., object detection, instance segmentation) and Vision-Language (VL) understanding tas…
Detection Hub: Unifying Object Detection Datasets via Query Adaptation on Language Embedding
Lingchen Meng, Xiyang Dai, Yinpeng Chen +7
Combining multiple datasets enables performance boost on many computer vision tasks. But similar trend has not been witnessed in object detection when combining multiple datasets d…
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…
Reduce Information Loss in Transformers for Pluralistic Image Inpainting
Qiankun Liu, Zhentao Tan, Dongdong Chen +6
Transformers have achieved great success in pluralistic image inpainting recently. However, we find existing transformer based solutions regard each pixel as a token, thus suffer f…
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,…
MiniViT: Compressing Vision Transformers with Weight Multiplexing
Jinnian Zhang, Houwen Peng, Kan Wu +4
Vision Transformer (ViT) models have recently drawn much attention in computer vision due to their high model capability. However, ViT models suffer from huge number of parameters,…
DaViT: Dual Attention Vision Transformers
Mingyu Ding, Bin Xiao, Noel Codella +3
In this work, we introduce Dual Attention Vision Transformers (DaViT), a simple yet effective vision transformer architecture that is able to capture global context while maintaini…