1 code implementation • 28 Nov 2023 • Zhihe Lu, Jiawang Bai, Xin Li, Zeyu Xiao, Xinchao Wang
However, performance advancements are limited when relying solely on intricate algorithmic designs for a single model, even one exhibiting strong performance, e. g., CLIP-ViT-B/16.
Ranked #2 on Prompt Engineering on ImageNet
no code implementations • 26 Nov 2023 • Jiawang Bai, Kuofeng Gao, Shaobo Min, Shu-Tao Xia, Zhifeng Li, Wei Liu
Contrastive Vision-Language Pre-training, known as CLIP, has shown promising effectiveness in addressing downstream image recognition tasks.
1 code implementation • NeurIPS 2023 • Xin Li, Dongze Lian, Zhihe Lu, Jiawang Bai, Zhibo Chen, Xinchao Wang
To mitigate that, we propose an effective adapter-style tuning strategy, dubbed GraphAdapter, which performs the textual adapter by explicitly modeling the dual-modality structure knowledge (i. e., the correlation of different semantics/classes in textual and visual modalities) with a dual knowledge graph.
no code implementations • 23 May 2023 • Zeyu Xiao, Jiawang Bai, Zhihe Lu, Zhiwei Xiong
This motivates the investigation and incorporation of prior knowledge in order to effectively constrain the solution space and enhance the quality of the restored images.
no code implementations • 11 May 2023 • Zhihe Lu, Zeyu Xiao, Jiawang Bai, Zhiwei Xiong, Xinchao Wang
To use the SAM-based prior, we propose a simple yet effective module -- SAM-guidEd refinEment Module (SEEM), which can enhance both alignment and fusion procedures by the utilization of semantic information.
no code implementations • 22 Mar 2023 • Xunguang Wang, Jiawang Bai, Xinyue Xu, Xiaomeng Li
Deep hashing has been extensively applied to massive image retrieval due to its efficiency and effectiveness.
1 code implementation • 17 Aug 2022 • Kuofeng Gao, Jiawang Bai, Baoyuan Wu, Mengxi Ya, Shu-Tao Xia
Existing attacks often insert some additional points into the point cloud as the trigger, or utilize a linear transformation (e. g., rotation) to construct the poisoned point cloud.
1 code implementation • 27 Jul 2022 • Jiawang Bai, Kuofeng Gao, Dihong Gong, Shu-Tao Xia, Zhifeng Li, Wei Liu
The security of deep neural networks (DNNs) has attracted increasing attention due to their widespread use in various applications.
1 code implementation • 25 Jul 2022 • Jiawang Bai, Baoyuan Wu, Zhifeng Li, Shu-Tao Xia
Utilizing the latest technique in integer programming, we equivalently reformulate this MIP problem as a continuous optimization problem, which can be effectively and efficiently solved using the alternating direction method of multipliers (ADMM) method.
1 code implementation • 3 Apr 2022 • Jiawang Bai, Li Yuan, Shu-Tao Xia, Shuicheng Yan, Zhifeng Li, Wei Liu
Inspired by this finding, we first investigate the effects of existing techniques for improving ViT models from a new frequency perspective, and find that the success of some techniques (e. g., RandAugment) can be attributed to the better usage of the high-frequency components.
Ranked #2 on Domain Generalization on Stylized-ImageNet
1 code implementation • 18 Sep 2021 • Kuofeng Gao, Jiawang Bai, Bin Chen, Dongxian Wu, Shu-Tao Xia
To this end, we propose the confusing perturbations-induced backdoor attack (CIBA).
no code implementations • ICML Workshop AML 2021 • Jiawang Bai, Bin Chen, Dongxian Wu, Chaoning Zhang, Shu-Tao Xia
We propose $universal \ adversarial \ head$ (UAH), which crafts adversarial query videos by prepending the original videos with a sequence of adversarial frames to perturb the normal hash codes in the Hamming space.
2 code implementations • ICLR 2021 • Jiawang Bai, Baoyuan Wu, Yong Zhang, Yiming Li, Zhifeng Li, Shu-Tao Xia
By utilizing the latest technique in integer programming, we equivalently reformulate this BIP problem as a continuous optimization problem, which can be effectively and efficiently solved using the alternating direction method of multipliers (ADMM) method.
2 code implementations • 12 Oct 2020 • Yiming Li, Ziqi Zhang, Jiawang Bai, Baoyuan Wu, Yong Jiang, Shu-Tao Xia
Based on the proposed backdoor-based watermarking, we use a hypothesis test guided method for dataset verification based on the posterior probability generated by the suspicious third-party model of the benign samples and their correspondingly watermarked samples ($i. e.$, images with trigger) on the target class.
no code implementations • 21 Aug 2020 • Yiming Li, Jiawang Bai, Jiawei Li, Xue Yang, Yong Jiang, Shu-Tao Xia
Interpretability and effectiveness are two essential and indispensable requirements for adopting machine learning methods in reality.
2 code implementations • ECCV 2020 • Jiawang Bai, Bin Chen, Yiming Li, Dongxian Wu, Weiwei Guo, Shu-Tao Xia, En-hui Yang
In this paper, we propose a novel method, dubbed deep hashing targeted attack (DHTA), to study the targeted attack on such retrieval.
no code implementations • 14 Mar 2019 • Jiawang Bai, Yiming Li, Jiawei Li, Yong Jiang, Shu-Tao Xia
How to obtain a model with good interpretability and performance has always been an important research topic.
no code implementations • 10 Mar 2019 • Yiming Li, Jiawang Bai, Jiawei Li, Xue Yang, Yong Jiang, Chun Li, Shu-Tao Xia
Despite the impressive performance of random forests (RF), its theoretical properties have not been thoroughly understood.