no code implementations • 15 Apr 2024 • Mude Hui, Siwei Yang, Bingchen Zhao, Yichun Shi, Heng Wang, Peng Wang, Yuyin Zhou, Cihang Xie
This study introduces HQ-Edit, a high-quality instruction-based image editing dataset with around 200, 000 edits.
1 code implementation • 13 Apr 2024 • Ye Wang, Yaxiong Wang, Yujiao Wu, Bingchen Zhao, Xueming Qian
To counteract this inefficiency, we opt to cluster only the unlabelled instances and subsequently expand the cluster prototypes with our introduced potential prototypes to fast explore novel classes.
4 code implementations • 8 Apr 2024 • Bo Peng, Daniel Goldstein, Quentin Anthony, Alon Albalak, Eric Alcaide, Stella Biderman, Eugene Cheah, Xingjian Du, Teddy Ferdinan, Haowen Hou, Przemysław Kazienko, Kranthi Kiran GV, Jan Kocoń, Bartłomiej Koptyra, Satyapriya Krishna, Ronald McClelland Jr., Niklas Muennighoff, Fares Obeid, Atsushi Saito, Guangyu Song, Haoqin Tu, Stanisław Woźniak, Ruichong Zhang, Bingchen Zhao, Qihang Zhao, Peng Zhou, Jian Zhu, Rui-Jie Zhu
We present Eagle (RWKV-5) and Finch (RWKV-6), sequence models improving upon the RWKV (RWKV-4) architecture.
no code implementations • 29 Mar 2024 • Yucheng Jin, Yun Xiong, Juncheng Fang, Xixi Wu, Dongxiao He, Xing Jia, Bingchen Zhao, Philip Yu
Inter-class correlations are subsequently eliminated by the prototypical attention network, leading to distinctive representations for different classes.
1 code implementation • 14 Feb 2024 • Siwei Yang, Bingchen Zhao, Cihang Xie
This paper introduces AQA-Bench, a novel benchmark to assess the sequential reasoning capabilities of large language models (LLMs) in algorithmic contexts, such as depth-first search (DFS).
no code implementations • 18 Dec 2023 • Bingchen Zhao, Haoqin Tu, Chen Wei, Jieru Mei, Cihang Xie
This paper introduces an efficient strategy to transform Large Language Models (LLMs) into Multi-Modal Large Language Models (MLLMs).
no code implementations • 11 Dec 2023 • Lei Zhang, Fangxun Shu, Sucheng Ren, Bingchen Zhao, Hao Jiang, Cihang Xie
The massive growth of image-text data through web crawling inherently presents the challenge of variability in data quality.
1 code implementation • 27 Nov 2023 • Haoqin Tu, Chenhang Cui, Zijun Wang, Yiyang Zhou, Bingchen Zhao, Junlin Han, Wangchunshu Zhou, Huaxiu Yao, Cihang Xie
Different from prior studies, we shift our focus from evaluating standard performance to introducing a comprehensive safety evaluation suite, covering both out-of-distribution (OOD) generalization and adversarial robustness.
1 code implementation • 10 Oct 2023 • Letian Zhang, Xiaotong Zhai, Zhongkai Zhao, Yongshuo Zong, Xin Wen, Bingchen Zhao
In light of the advancements in current multi-modal large language models, we explore their effectiveness in counterfactual reasoning.
1 code implementation • 2 Oct 2023 • Yongshuo Zong, Tingyang Yu, Bingchen Zhao, Ruchika Chavhan, Timothy Hospedales
Large language and vision-language models are rapidly being deployed in practice thanks to their impressive capabilities in instruction following, in-context learning, and so on.
1 code implementation • 13 Sep 2023 • Haoqin Tu, Bingchen Zhao, Chen Wei, Cihang Xie
Multi-modal large language models (MLLMs) are trained based on large language models (LLM), with an enhanced capability to comprehend multi-modal inputs and generate textual responses.
1 code implementation • ICCV 2023 • Bingchen Zhao, Xin Wen, Kai Han
In this paper, we address the problem of generalized category discovery (GCD), \ie, given a set of images where part of them are labelled and the rest are not, the task is to automatically cluster the images in the unlabelled data, leveraging the information from the labelled data, while the unlabelled data contain images from the labelled classes and also new ones.
no code implementations • ICCV 2023 • Bingchen Zhao, Oisin Mac Aodha
We explore the problem of Incremental Generalized Category Discovery (IGCD).
no code implementations • 17 Apr 2023 • Bingchen Zhao, Jiahao Wang, Wufei Ma, Artur Jesslen, Siwei Yang, Shaozuo Yu, Oliver Zendel, Christian Theobalt, Alan Yuille, Adam Kortylewski
Enhancing the robustness of vision algorithms in real-world scenarios is challenging.
no code implementations • 17 Jan 2023 • Bingchen Zhao, Quan Cui, Hao Wu, Osamu Yoshie, Cheng Yang, Oisin Mac Aodha
In this work, given the excellent scalability of web data, we consider self-supervised pre-training on noisy web sourced image-text paired data.
2 code implementations • ICCV 2023 • Xin Wen, Bingchen Zhao, Xiaojuan Qi
Generalized Category Discovery (GCD) aims to discover novel categories in unlabelled datasets using knowledge learned from labelled samples.
Ranked #1 on Open-World Semi-Supervised Learning on ImageNet-100
no code implementations • 17 Nov 2022 • Bingchen Zhao, Yuling Gu, Jessica Zosa Forde, Naomi Saphra
At NeurIPS, American and Chinese institutions cite papers from each other's regions substantially less than they cite endogamously.
1 code implementation • 3 Aug 2022 • Yixin Fei, Zhongkai Zhao, Siwei Yang, Bingchen Zhao
We address the problem of generalized category discovery (GCD) in this paper, i. e. clustering the unlabeled images leveraging the information from a set of seen classes, where the unlabeled images could contain both seen classes and unseen classes.
1 code implementation • 30 May 2022 • Xin Wen, Bingchen Zhao, Anlin Zheng, Xiangyu Zhang, Xiaojuan Qi
The semantic grouping is performed by assigning pixels to a set of learnable prototypes, which can adapt to each sample by attentive pooling over the feature and form new slots.
Ranked #15 on Unsupervised Semantic Segmentation on COCO-Stuff-27 (Accuracy metric)
1 code implementation • 8 Mar 2022 • Quan Cui, Bingchen Zhao, Zhao-Min Chen, Borui Zhao, RenJie Song, Jiajun Liang, Boyan Zhou, Osamu Yoshie
This work simultaneously considers the discriminability and transferability properties of deep representations in the typical supervised learning task, i. e., image classification.
no code implementations • 29 Nov 2021 • Bingchen Zhao, Shaozuo Yu, Wufei Ma, Mingxin Yu, Shenxiao Mei, Angtian Wang, Ju He, Alan Yuille, Adam Kortylewski
One reason is that existing robustness benchmarks are limited, as they either rely on synthetic data or ignore the effects of individual nuisance factors.
1 code implementation • ICCV 2021 • Rui Zhu, Bingchen Zhao, Jingen Liu, Zhenglong Sun, Chang Wen Chen
To our knowledge, this is the first attempt of its kind.
no code implementations • NeurIPS 2021 • Bingchen Zhao, Kai Han
In this paper, we tackle the problem of novel visual category discovery, i. e., grouping unlabelled images from new classes into different semantic partitions by leveraging a labelled dataset that contains images from other different but relevant categories.
no code implementations • 28 Jun 2021 • Zihao Zhang, Shaozuo Yu, Siwei Yang, Yu Zhou, Bingchen Zhao
This paper presents the Rail-5k dataset for benchmarking the performance of visual algorithms in a real-world application scenario, namely the rail surface defects detection task.
1 code implementation • 6 Jun 2021 • Siwei Yang, Shaozuo Yu, Bingchen Zhao, Yin Wang
Visual pattern recognition over agricultural areas is an important application of aerial image processing.
1 code implementation • 4 Aug 2020 • Jie Shao, Xin Wen, Bingchen Zhao, xiangyang xue
The current research focus on Content-Based Video Retrieval requires higher-level video representation describing the long-range semantic dependencies of relevant incidents, events, etc.
Ranked #6 on Video Retrieval on FIVR-200K
1 code implementation • 1 Aug 2020 • Bingchen Zhao, Xin Wen
Convolutional Neural Networks (CNNs) are prone to overfit small training datasets.
1 code implementation • 21 Apr 2020 • Mang Tik Chiu, Xingqian Xu, Kai Wang, Jennifer Hobbs, Naira Hovakimyan, Thomas S. Huang, Honghui Shi, Yunchao Wei, Zilong Huang, Alexander Schwing, Robert Brunner, Ivan Dozier, Wyatt Dozier, Karen Ghandilyan, David Wilson, Hyunseong Park, Junhee Kim, Sungho Kim, Qinghui Liu, Michael C. Kampffmeyer, Robert Jenssen, Arnt B. Salberg, Alexandre Barbosa, Rodrigo Trevisan, Bingchen Zhao, Shaozuo Yu, Siwei Yang, Yin Wang, Hao Sheng, Xiao Chen, Jingyi Su, Ram Rajagopal, Andrew Ng, Van Thong Huynh, Soo-Hyung Kim, In-Seop Na, Ujjwal Baid, Shubham Innani, Prasad Dutande, Bhakti Baheti, Sanjay Talbar, Jianyu Tang
The first Agriculture-Vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially for the semantic segmentation task associated with our challenge dataset.