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20152024
most citedLearning Spatio-Temporal Representation with Pseudo-3D Residual Networks

251 citations · 1k across the 82 of their papers we have counts for

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Showing 2023Show all

7 papers · 1 filter

cs.CV2023★ 4 cited

Bidirectional Knowledge Reconfiguration for Lightweight Point Cloud Analysis

Peipei Li, Xing Cui, Yibo Hu +3

Point cloud analysis faces computational system overhead, limiting its application on mobile or edge devices. Directly employing small models may result in a significant drop in pe…

cs.CV2023★ 2 cited

Selective Volume Mixup for Video Action Recognition

Yi Tan, Zhaofan Qiu, Yanbin Hao +2

The recent advances in Convolutional Neural Networks (CNNs) and Vision Transformers have convincingly demonstrated high learning capability for video action recognition on large da…

cs.CV2023★ 4 cited

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…

cs.CV2023★ 2 cited

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…

eess.AS2023★ 2 cited

Visual-Aware Text-to-Speech

Mohan Zhou, Yalong Bai, Wei Zhang +3

Dynamically synthesizing talking speech that actively responds to a listening head is critical during the face-to-face interaction. For example, the speaker could take advantage of…

cs.CV2023★ 1 cited

Transforming Radiance Field with Lipschitz Network for Photorealistic 3D Scene Stylization

Zicheng Zhang, Yinglu Liu, Congying Han +3

Recent advances in 3D scene representation and novel view synthesis have witnessed the rise of Neural Radiance Fields (NeRFs). Nevertheless, it is not trivial to exploit NeRF for t…

cs.CV2023★ 1 cited

Modality-Agnostic Debiasing for Single Domain Generalization

Sanqing Qu, Yingwei Pan, Guang Chen +3

Deep neural networks (DNNs) usually fail to generalize well to outside of distribution (OOD) data, especially in the extreme case of single domain generalization (single-DG) that t…