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Anima2: Cross-Species Animal Animation through Image-to-Video Synthesis with Subject Alignment
Yuanfeng Xu;Yuhao Chen;Zhongzhan Huang;Zijian He;Guangrun Wang;Liang Lin
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
Recent video editing advancements rely on accurate pose sequences to animate human actors. However, these efforts are not suitable for cross-species animation due to pose misalignment between species (for example, the poses of a cat differ greatly from that of a pig due to their distinct body structures). In this paper, we present Anima2, a zero-shot diffusion-based video generator to address this...Show More
A Causality-Aware Paradigm for Evaluating Creativity of Multimodal Large Language Models
Zhongzhan Huang;Shanshan Zhong;Pan Zhou;Shanghua Gao;Marinka Zitnik;Liang Lin
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2025 | Early Access Article |
Recently, numerous benchmarks have been developed to evaluate the logical reasoning abilities of large language models (LLMs). However, assessing the equally important creative capabilities of LLMs is challenging due to the subjective, diverse, and data-scarce nature of creativity, especially in multimodal scenarios. In this paper, we consider the comprehensive pipeline for evaluating the creativi...Show More
Lottery Ticket Hypothesis for Attention Mechanism in Residual Convolutional Neural Network*
Zhongzhan Huang;Senwei Liang;Mingfu Liang;Wei He;Haizhao Yang;Liang Lin
2024 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2024 | Conference Paper |
Recently many plug-and-play self-attention modules (SAMs) are proposed to enhance the model performance by exploiting the internal information of deep convolutional neural networks. However, most SAMs connect individually with each block of the backbone for granted, leading to incremental computational cost and the number of parameters with the growth of network depth. To address this issue, we fi...Show More
Let's Think Outside the Box: Exploring Leap-of-Thought in Large Language Models with Creative Humor Generation
Shanshan Zhong;Zhongzhan Huang;Shanghua Gao;Wushao Wen;Liang Lin;Marinka Zitnik;Pan Zhou
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
Chain-of-Thought (CoT) [2], [3] guides large language models (LLMs) to reason step-by-step, and can motivate their logical reasoning ability. While effective for logi-cal tasks, CoT is not conducive to creative problem-solving which often requires out-of-box thoughts and is crucial for innovation advancements. In this paper, we explore the Leap-of-Thought (LoT) abilities within LLMs - a non-sequen...Show More
Understanding Self-attention Mechanism via Dynamical System Perspective
Zhongzhan Huang;Mingfu Liang;Jinghui Qin;Shanshan Zhong;Liang Lin
2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (7)
The self-attention mechanism (SAM) is widely used in various fields of artificial intelligence and has successfully boosted the performance of different models. However, current explanations of this mechanism are mainly based on intuitions and experiences, while there still lacks direct modeling for how the SAM helps performance. To mitigate this issue, in this paper, based on the dynamical system...Show More
LSAS: Lightweight Sub-attention Strategy for Alleviating Attention Bias Problem
Shanshan Zhong;Wushao Wen;Jinghui Qin;Qiangpu Chen;Zhongzhan Huang
2023 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2023 | Conference Paper |
Cited by: Papers (3)
In computer vision, the performance of deep neural networks (DNNs) is highly related to the feature extraction ability, i.e., the ability to recognize and focus on key pixel regions in an image. However, in this paper, we quantitatively and statistically illustrate that DNNs have a serious attention bias problem on many samples from some popular datasets: (1) Position bias: DNNs fully focus on lab...Show More
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