52 citations · 143 across the 17 of their papers we have counts for
17 papers · 1 filter
Learning and Evaluating Human Preferences for Conversational Head Generation
Mohan Zhou, Yalong Bai, Wei Zhang +3
A reliable and comprehensive evaluation metric that aligns with manual preference assessments is crucial for conversational head video synthesis methods development. Existing quant…
Interactive Conversational Head Generation
Mohan Zhou, Yalong Bai, Wei Zhang +2
We introduce a new conversation head generation benchmark for synthesizing behaviors of a single interlocutor in a face-to-face conversation. The capability to automatically synthe…
Deep Equilibrium Multimodal Fusion
Jinhong Ni, Yalong Bai, Wei Zhang +2
Multimodal fusion integrates the complementary information present in multiple modalities and has gained much attention recently. Most existing fusion approaches either learn a fix…
Learning cross space mapping via DNN using large scale click-through logs
Wei Yu, Kuiyuan Yang, Yalong Bai +2
The gap between low-level visual signals and high-level semantics has been progressively bridged by continuous development of deep neural network (DNN). With recent progress of DNN…
Visualizing and Understanding Patch Interactions in Vision Transformer
Jie Ma, Yalong Bai, Bineng Zhong +3
Vision Transformer (ViT) has become a leading tool in various computer vision tasks, owing to its unique self-attention mechanism that learns visual representations explicitly thro…
Freeform Body Motion Generation from Speech
Jing Xu, Wei Zhang, Yalong Bai +2
People naturally conduct spontaneous body motions to enhance their speeches while giving talks. Body motion generation from speech is inherently difficult due to the non-determinis…
Responsive Listening Head Generation: A Benchmark Dataset and Baseline
Mohan Zhou, Yalong Bai, Wei Zhang +3
We present a new listening head generation benchmark, for synthesizing responsive feedbacks of a listener (e.g., nod, smile) during a face-to-face conversation. As the indispensabl…
Directional Self-supervised Learning for Heavy Image Augmentations
Yalong Bai, Yifan Yang, Wei Zhang +1
Despite the large augmentation family, only a few cherry-picked robust augmentation policies are beneficial to self-supervised image representation learning. In this paper, we prop…
Augmentation Pathways Network for Visual Recognition
Yalong Bai, Mohan Zhou, Wei Zhang +2
Data augmentation is practically helpful for visual recognition, especially at the time of data scarcity. However, such success is only limited to quite a few light augmentations (…
Exploiting Relationship for Complex-scene Image Generation
Tianyu Hua, Hongdong Zheng, Yalong Bai +3
The significant progress on Generative Adversarial Networks (GANs) has facilitated realistic single-object image generation based on language input. However, complex-scene generati…
Products-10K: A Large-scale Product Recognition Dataset
Yalong Bai, Yuxiang Chen, Wei Yu +2
With the rapid development of electronic commerce, the way of shopping has experienced a revolutionary evolution. To fully meet customers' massive and diverse online shopping needs…
Look-into-Object: Self-supervised Structure Modeling for Object Recognition
Mohan Zhou, Yalong Bai, Wei Zhang +2
Most object recognition approaches predominantly focus on learning discriminative visual patterns while overlooking the holistic object structure. Though important, structure model…
Relationship-Aware Spatial Perception Fusion for Realistic Scene Layout Generation
Hongdong Zheng, Yalong Bai, Wei Zhang +1
The significant progress on Generative Adversarial Networks (GANs) have made it possible to generate surprisingly realistic images for single object based on natural language descr…
VrR-VG: Refocusing Visually-Relevant Relationships
Yuanzhi Liang, Yalong Bai, Wei Zhang +3
Relationships encode the interactions among individual instances, and play a critical role in deep visual scene understanding. Suffering from the high predictability with non-visua…
Automatic Dataset Augmentation
Yalong Bai, Kuiyuan Yang, Tao Mei +2
Large scale image dataset and deep convolutional neural network (DCNN) are two primary driving forces for the rapid progress made in generic object recognition tasks in recent year…
Visualizing and Comparing Convolutional Neural Networks
Wei Yu, Kuiyuan Yang, Yalong Bai +2
Convolutional Neural Networks (CNNs) have achieved comparable error rates to well-trained human on ILSVRC2014 image classification task. To achieve better performance, the complexi…
Learning High-level Image Representation for Image Retrieval via Multi-Task DNN using Clickthrough Data
Yalong Bai, Kuiyuan Yang, Wei Yu +2
Image retrieval refers to finding relevant images from an image database for a query, which is considered difficult for the gap between low-level representation of images and high-…