1 code implementation • 18 Apr 2024 • Jiabao Ji, Bairu Hou, Zhen Zhang, Guanhua Zhang, Wenqi Fan, Qing Li, Yang Zhang, Gaowen Liu, Sijia Liu, Shiyu Chang
Although large language models (LLMs) have achieved significant success, their vulnerability to adversarial perturbations, including recent jailbreak attacks, has raised considerable concerns.
1 code implementation • 8 Apr 2024 • Matteo Farina, Massimiliano Mancini, Elia Cunegatti, Gaowen Liu, Giovanni Iacca, Elisa Ricci
In this challenging setting, the transferable representations already encoded in the pretrained model are a key aspect to preserve.
no code implementations • 7 Apr 2024 • Hou-I Liu, Christine Wu, Jen-Hao Cheng, Wenhao Chai, Shian-Yun Wang, Gaowen Liu, Jenq-Neng Hwang, Hong-Han Shuai, Wen-Huang Cheng
Subsequently, we introduce the cross-modal residual distillation to transfer the 3D spatial cues.
1 code implementation • 2 Apr 2024 • Sihao Hu, Tiansheng Huang, Fatih Ilhan, Selim Tekin, Gaowen Liu, Ramana Kompella, Ling Liu
The development of game agents holds a critical role in advancing towards Artificial General Intelligence (AGI).
no code implementations • 31 Mar 2024 • Wenfang Sun, Yingjun Du, Gaowen Liu, Ramana Kompella, Cees G. M. Snoek
Additionally, we propose an assembly that merges the segmentation maps from the various subclass descriptors to ensure a more comprehensive representation of the different aspects in the test images.
no code implementations • 18 Mar 2024 • Junge Zhang, Qihang Zhang, Li Zhang, Ramana Rao Kompella, Gaowen Liu, Bolei Zhou
Generating unbounded 3D scenes is crucial for large-scale scene understanding and simulation.
1 code implementation • 7 Mar 2024 • Kaiwen Cai, Zhekai Duan, Gaowen Liu, Charles Fleming, Chris Xiaoxuan Lu
Recent advancements in Vision-Language (VL) models have sparked interest in their deployment on edge devices, yet challenges in handling diverse visual modalities, manual annotation, and computational constraints remain.
1 code implementation • 15 Feb 2024 • Letian Peng, Yuwei Zhang, Zilong Wang, Jayanth Srinivasa, Gaowen Liu, Zihan Wang, Jingbo Shang
This work aims to build a text embedder that can capture characteristics of texts specified by user instructions.
1 code implementation • 4 Nov 2023 • Zhuoshi Pan, Yuguang Yao, Gaowen Liu, Bingquan Shen, H. Vicky Zhao, Ramana Rao Kompella, Sijia Liu
We show that this latter insight can be used to enhance the detection of backdoor-poisoned training data.
no code implementations • 2 Nov 2023 • Jen-Hao Cheng, Sheng-Yao Kuan, Hugo Latapie, Gaowen Liu, Jenq-Neng Hwang
CenterRadarNet achieves the state-of-the-art result on the K-Radar 3D object detection benchmark.
1 code implementation • ICCV 2023 • Yuzhang Shang, Bingxin Xu, Gaowen Liu, Ramana Kompella, Yan Yan
Inspired by the causal understanding, we propose the Causality-guided Data-free Network Quantization method, Causal-DFQ, to eliminate the reliance on data via approaching an equilibrium of causality-driven intervened distributions.
1 code implementation • 6 Sep 2023 • Sanjana Vijay Ganesh, Yanzhao Wu, Gaowen Liu, Ramana Kompella, Ling Liu
Object tracking is an important functionality of edge video analytic systems and services.
no code implementations • 15 Aug 2023 • Peihao Chen, Xinyu Sun, Hongyan Zhi, Runhao Zeng, Thomas H. Li, Gaowen Liu, Mingkui Tan, Chuang Gan
We study the task of zero-shot vision-and-language navigation (ZS-VLN), a practical yet challenging problem in which an agent learns to navigate following a path described by language instructions without requiring any path-instruction annotation data.
1 code implementation • 18 May 2023 • Ziheng Chen, Yue Song, Gaowen Liu, Ramana Rao Kompella, XiaoJun Wu, Nicu Sebe
Besides, our framework offers a novel intrinsic explanation for the most popular LogEig classifier in existing SPD networks.
1 code implementation • NeurIPS 2023 • Jinghan Jia, Jiancheng Liu, Parikshit Ram, Yuguang Yao, Gaowen Liu, Yang Liu, Pranay Sharma, Sijia Liu
We show in both theory and practice that model sparsity can boost the multi-criteria unlearning performance of an approximate unlearner, closing the approximation gap, while continuing to be efficient.
no code implementations • 27 Jan 2023 • Bin Duan, Keshav Bhandari, Gaowen Liu, Yan Yan
Moreover, we present a novel Siamese representation Learning framework for Omnidirectional Flow (SLOF) estimation, which is trained in a contrastive manner via a hybrid loss that combines siamese contrastive and optical flow losses.
1 code implementation • 15 Jan 2023 • Fatih Ilhan, Ka-Ho Chow, Sihao Hu, Tiansheng Huang, Selim Tekin, Wenqi Wei, Yanzhao Wu, Myungjin Lee, Ramana Kompella, Hugo Latapie, Gaowen Liu, Ling Liu
Instead of having every sample go through all DNN layers during prediction, EENet learns an early exit scheduler, which can intelligently terminate the inference earlier for certain predictions, which the model has high confidence of early exit.
no code implementations • 7 Aug 2022 • Keshav Bhandari, Bin Duan, Gaowen Liu, Hugo Latapie, Ziliang Zong, Yan Yan
Optical flow estimation in omnidirectional videos faces two significant issues: the lack of benchmark datasets and the challenge of adapting perspective video-based methods to accommodate the omnidirectional nature.
no code implementations • 7 Jul 2021 • Gaowen Liu, Hao Tang, Hugo Latapie, Jason Corso, Yan Yan
Particularly, we propose a novel Bi-directional Spatial Temporal Attention Fusion Generative Adversarial Network (STA-GAN) to learn both spatial and temporal information to generate egocentric video sequences from the exocentric view.
no code implementations • 11 Feb 2021 • Hugo Latapie, Ozkan Kilic, Gaowen Liu, Yan Yan, Ramana Kompella, Pei Wang, Kristinn R. Thorisson, Adam Lawrence, Yuhong Sun, Jayanth Srinivasa
This paper introduces a new metamodel-based knowledge representation that significantly improves autonomous learning and adaptation.
no code implementations • 8 Feb 2020 • Gaowen Liu, Hao Tang, Hugo Latapie, Yan Yan
In this paper, we investigate exocentric (third-person) view to egocentric (first-person) view image generation.
1 code implementation • 2 Aug 2019 • Hao Tang, Dan Xu, Gaowen Liu, Wei Wang, Nicu Sebe, Yan Yan
In this work, we propose a novel Cycle In Cycle Generative Adversarial Network (C$^2$GAN) for the task of keypoint-guided image generation.