Author name cluster

Yehao Li

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4 papers

2 author rows

ICML Conference 2025 Conference Paper

Hierarchical Masked Autoregressive Models with Low-Resolution Token Pivots

Guangting Zheng
Yehao Li
Yingwei Pan
Jiajun Deng
Ting Yao 0003
Yanyong Zhang
Tao Mei 0001

Autoregressive models have emerged as a powerful generative paradigm for visual generation. The current de-facto standard of next token prediction commonly operates over a single-scale sequence of dense image tokens, and is incapable of utilizing global context especially for early tokens prediction. In this paper, we introduce a new autoregressive design to model a hierarchy from a few low-resolution image tokens to the typical dense image tokens, and delve into a thorough hierarchical dependency across multi-scale image tokens. Technically, we present a Hierarchical Masked Autoregressive models (Hi-MAR) that pivot on low-resolution image tokens to trigger hierarchical autoregressive modeling in a multi-phase manner. Hi-MAR learns to predict a few image tokens in low resolution, functioning as intermediary pivots to reflect global structure, in the first phase. Such pivots act as the additional guidance to strengthen the next autoregressive modeling phase by shaping global structural awareness of typical dense image tokens. A new Diffusion Transformer head is further devised to amplify the global context among all tokens for mask token prediction. Extensive evaluations on both class-conditional and text-to-image generation tasks demonstrate that Hi-MAR outperforms typical AR baselines, while requiring fewer computational costs.

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AAAI Conference 2021 Conference Paper

Scheduled Sampling in Vision-Language Pretraining with Decoupled Encoder-Decoder Network

Yehao Li
Yingwei Pan
Ting Yao
Jingwen Chen
Tao Mei

Despite having impressive vision-language (VL) pretraining with BERT-based encoder for VL understanding, the pretraining of a universal encoder-decoder for both VL understanding and generation remains challenging. The difficulty originates from the inherently different peculiarities of the two disciplines, e. g. , VL understanding tasks capitalize on the unrestricted message passing across modalities, while generation tasks only employ visual-to-textual message passing. In this paper, we start with a two-stream decoupled design of encoder-decoder structure, in which two decoupled cross-modal encoder and decoder are involved to separately perform each type of proxy tasks, for simultaneous VL understanding and generation pretraining. Moreover, for VL pretraining, the dominant way is to replace some input visual/word tokens with mask tokens and enforce the multimodal encoder/decoder to reconstruct the original tokens, but no mask token is involved when fine-tuning on downstream tasks. As an alternative, we propose a primary scheduled sampling strategy that elegantly mitigates such discrepancy via pretraining encoder-decoder in a two-pass manner. Extensive experiments demonstrate the compelling generalizability of our pretrained encoder-decoder by fine-tuning on four VL understanding and generation downstream tasks. Source code is available at https: //github. com/YehLi/TDEN.

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AAAI Conference 2019 Conference Paper

Temporal Deformable Convolutional Encoder-Decoder Networks for Video Captioning

Jingwen Chen
Yingwei Pan
Yehao Li
Ting Yao
Hongyang Chao
Tao Mei

It is well believed that video captioning is a fundamental but challenging task in both computer vision and artificial intelligence fields. The prevalent approach is to map an input video to a variable-length output sentence in a sequence to sequence manner via Recurrent Neural Network (RNN). Nevertheless, the training of RNN still suffers to some degree from vanishing/exploding gradient problem, making the optimization difficult. Moreover, the inherently recurrent dependency in RNN prevents parallelization within a sequence during training and therefore limits the computations. In this paper, we present a novel design — Temporal Deformable Convolutional Encoder-Decoder Networks (dubbed as TD- ConvED) that fully employ convolutions in both encoder and decoder networks for video captioning. Technically, we exploit convolutional block structures that compute intermediate states of a fixed number of inputs and stack several blocks to capture long-term relationships. The structure in encoder is further equipped with temporal deformable convolution to enable free-form deformation of temporal sampling. Our model also capitalizes on temporal attention mechanism for sentence generation. Extensive experiments are conducted on both MSVD and MSR-VTT video captioning datasets, and superior results are reported when comparing to conventional RNN-based encoder-decoder techniques. More remarkably, TDConvED increases CIDEr-D performance from 58. 8% to 67. 2% on MSVD.

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IJCAI Conference 2016 Conference Paper

Learning Deep Intrinsic Video Representation by Exploring Temporal Coherence and Graph Structure

Yingwei Pan
Yehao Li
Ting Yao
Tao Mei
Houqiang Li
Yong Rui

Learning video representation is not a trivial task, as video is an information-intensive media where each frame does not exist independently. Locally, a video frame is visually and semantically similar with its adjacent frames. Holistically, a video has its inherent structure - the correlations among video frames. For example, even the frames far from each other may also hold similar semantics. Such context information is therefore important to characterize the intrinsic representation of a video frame. In this paper, we present a novel approach to learn the deep video representation by exploring both local and holistic contexts. Specifically, we propose a triplet sampling mechanism to encode the local temporal relationship of adjacent frames based on their deep representations. In addition, we incorporate the graph structure of the video, as a priori, to holistically preserve the inherent correlations among video frames. Our approach is fully unsupervised and trained in an end-to-end deep convolutional neural network architecture. By extensive experiments, we show that our learned representation can significantly boost several video recognition tasks (retrieval, classification, and highlight detection) over traditional video representations.

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