Search Results for author: Xiang Li

Found 397 papers, 143 papers with code

Multi-Modal Sarcasm Detection via Cross-Modal Graph Convolutional Network

1 code implementation ACL 2022 Bin Liang, Chenwei Lou, Xiang Li, Min Yang, Lin Gui, Yulan He, Wenjie Pei, Ruifeng Xu

Then, the descriptions of the objects are served as a bridge to determine the importance of the association between the objects of image modality and the contextual words of text modality, so as to build a cross-modal graph for each multi-modal instance.

Sarcasm Detection

BIT-Xiaomi’s System for AutoSimTrans 2022

no code implementations NAACL (AutoSimTrans) 2022 Mengge Liu, Xiang Li, Bao Chen, Yanzhi Tian, Tianwei Lan, Silin Li, Yuhang Guo, Jian Luan, Bin Wang

This system paper describes the BIT-Xiaomi simultaneous translation system for Autosimtrans 2022 simultaneous translation challenge.

Chunking Data Augmentation +1

融合情感分析的隐式反问句识别模型(Implicit Rhetorical Questions Recognition Model Combined with Sentiment Analysis)

no code implementations CCL 2021 Xiang Li, Chengwei Liu, Xiaoxu Zhu

“反问是现代汉语中一种常用的修辞手法, 根据是否含有反问标记可分为显式反问句与隐式反问句。其中隐式反问句表达的情感更为丰富, 表现形式也十分复杂, 对隐式反问句的识别更具挑战性。本文首先扩充了汉语反问句语料库, 语料库规模达到10000余句, 接着针对隐式反问句的特点, 提出了一种融合情感分析的隐式反问句识别模型。模型考虑了句子的语义信息, 上下文信息, 并借助情感分析任务辅助识别隐式反问句。实验结果表明, 本文提出的模型在隐式反问句识别任务上取得了良好的性能。”

Sentiment Analysis

Towards Robust Neural Machine Translation with Iterative Scheduled Data-Switch Training

1 code implementation COLING 2022 Zhongjian Miao, Xiang Li, Liyan Kang, Wen Zhang, Chulun Zhou, Yidong Chen, Bin Wang, Min Zhang, Jinsong Su

Most existing methods on robust neural machine translation (NMT) construct adversarial examples by injecting noise into authentic examples and indiscriminately exploit two types of examples.

Machine Translation NMT +1

Gait Recognition from a Single Image using a Phase-Aware Gait Cycle Reconstruction Network

no code implementations ECCV 2020 Chi Xu, Yasushi Makihara, Xiang Li, Yasushi Yagi, Jianfeng Lu

Specifically, a phase estimation network is introduced for the input single image, and the gait cycle reconstruction network exploits the estimated phase to mitigate the dependence of an encoded feature on the phase of that single image.

Gait Recognition

JointCL: A Joint Contrastive Learning Framework for Zero-Shot Stance Detection

1 code implementation ACL 2022 Bin Liang, Qinglin Zhu, Xiang Li, Min Yang, Lin Gui, Yulan He, Ruifeng Xu

In this paper, we propose a joint contrastive learning (JointCL) framework, which consists of stance contrastive learning and target-aware prototypical graph contrastive learning.

Contrastive Learning Stance Detection

Probabilistic Copyright Protection Can Fail for Text-to-Image Generative Models

1 code implementation29 Nov 2023 Xiang Li, Qianli Shen, Kenji Kawaguchi

The booming use of text-to-image generative models has raised concerns about their high risk of producing copyright-infringing content.

Federated Learning via Consensus Mechanism on Heterogeneous Data: A New Perspective on Convergence

1 code implementation21 Nov 2023 Shu Zheng, Tiandi Ye, Xiang Li, Ming Gao

We theoretically show that the consensus mechanism can guarantee the convergence of the global objective.

Fairness Federated Learning

Jailbreaking GPT-4V via Self-Adversarial Attacks with System Prompts

no code implementations15 Nov 2023 Yuanwei Wu, Xiang Li, Yixin Liu, Pan Zhou, Lichao Sun

Furthermore, in pursuit of better performance, we also add human modification based on GPT-4's analysis, which further improves the attack success rate to 98. 7\%; 3) We evaluated the effect of modifying system prompts to defend against jailbreaking attacks.

Adversarial Attack

Method for Text Entity Linking in Power Distribution Scheduling Oriented to Power Distribution Network Knowledge Graph

no code implementations15 Nov 2023 Xiang Li, Che Wang, Bing Li, Hao Chen, Sizhe Li

The proposed method for linking entities in power distribution dispatch texts to a power distribution network knowledge graph is based on a deep understanding of these networks.

Entity Linking Scheduling

Self-supervised Heterogeneous Graph Variational Autoencoders

no code implementations14 Nov 2023 Yige Zhao, Jianxiang Yu, Yao Cheng, Chengcheng Yu, Yiding Liu, Xiang Li, Shuaiqiang Wang

Instead of directly reconstructing raw features for attributed nodes, SHAVA generates the initial low-dimensional representation matrix for all the nodes, based on which raw features of attributed nodes are further reconstructed to leverage accurate attributes.

Graph Mining

CBSiMT: Mitigating Hallucination in Simultaneous Machine Translation with Weighted Prefix-to-Prefix Training

no code implementations7 Nov 2023 Mengge Liu, Wen Zhang, Xiang Li, Yanzhi Tian, Yuhang Guo, Jian Luan, Bin Wang, Shuoying Chen

Simultaneous machine translation (SiMT) is a challenging task that requires starting translation before the full source sentence is available.

Machine Translation Translation

Exploiting Latent Attribute Interaction with Transformer on Heterogeneous Information Networks

no code implementations6 Nov 2023 Zeyuan Zhao, Qingqing Ge, Anfeng Cheng, Yiding Liu, Xiang Li, Shuaiqiang Wang

In addition, most of them only consider the interactions between nodes while neglecting the high-order information behind the latent interactions among different node features.

Parameter-Agnostic Optimization under Relaxed Smoothness

no code implementations6 Nov 2023 Florian Hübler, Junchi Yang, Xiang Li, Niao He

However, as the assumption is relaxed to the more realistic $(L_0, L_1)$-smoothness, all existing convergence results still necessitate tuning of the stepsize.

Prioritized Propagation in Graph Neural Networks

no code implementations6 Nov 2023 Yao Cheng, Minjie Chen, Xiang Li, Caihua Shan, Ming Gao

Specifically, the framework consists of three components: a backbone GNN model, a propagation controller to determine the optimal propagation steps for nodes, and a weight controller to compute the priority scores for nodes.

High-resolution power equipment recognition based on improved self-attention

no code implementations6 Nov 2023 Siyi Zhang, Cheng Liu, Xiang Li, Xin Zhai, Zhen Wei, Sizhe Li, Xun Ma

The current trend of automating inspections at substations has sparked a surge in interest in the field of transformer image recognition.

Region Proposal

Retrieval-Augmented Code Generation for Universal Information Extraction

no code implementations6 Nov 2023 Yucan Guo, Zixuan Li, Xiaolong Jin, Yantao Liu, Yutao Zeng, Wenxuan Liu, Xiang Li, Pan Yang, Long Bai, Jiafeng Guo, Xueqi Cheng

Therefore, in this paper, we propose a universal retrieval-augmented code generation framework based on LLMs, called Code4UIE, for IE tasks.

Code Generation Retrieval

Resist Label Noise with PGM for Graph Neural Networks

no code implementations3 Nov 2023 Qingqing Ge, Jianxiang Yu, Zeyuan Zhao, Xiang Li

To further leverage the information of clean labels in the noisy label set, we put forward LNP-v2, which incorporates the noisy label set into the Bayesian network to generate clean labels.

Limited Data, Unlimited Potential: A Study on ViTs Augmented by Masked Autoencoders

no code implementations31 Oct 2023 Srijan Das, Tanmay Jain, Dominick Reilly, Pranav Balaji, Soumyajit Karmakar, Shyam Marjit, Xiang Li, Abhijit Das, Michael Ryoo

We explore the appropriate SSL tasks that can be optimized alongside the primary task, the training schemes for these tasks, and the data scale at which they can be most effective.

DeepFake Detection Face Swapping +1

Multimodal ChatGPT for Medical Applications: an Experimental Study of GPT-4V

1 code implementation29 Oct 2023 Zhiling Yan, Kai Zhang, Rong Zhou, Lifang He, Xiang Li, Lichao Sun

In this paper, we critically evaluate the capabilities of the state-of-the-art multimodal large language model, i. e., GPT-4 with Vision (GPT-4V), on Visual Question Answering (VQA) task.

Language Modelling Large Language Model +2

3DCoMPaT$^{++}$: An improved Large-scale 3D Vision Dataset for Compositional Recognition

no code implementations27 Oct 2023 Habib Slim, Xiang Li, Yuchen Li, Mahmoud Ahmed, Mohamed Ayman, Ujjwal Upadhyay, Ahmed Abdelreheem, Arpit Prajapati, Suhail Pothigara, Peter Wonka, Mohamed Elhoseiny

In this work, we present 3DCoMPaT$^{++}$, a multimodal 2D/3D dataset with 160 million rendered views of more than 10 million stylized 3D shapes carefully annotated at the part-instance level, alongside matching RGB point clouds, 3D textured meshes, depth maps, and segmentation masks.

Label Propagation for Graph Label Noise

no code implementations25 Oct 2023 Yao Cheng, Caihua Shan, Yifei Shen, Xiang Li, Siqiang Luo, Dongsheng Li

In this paper, we study graph label noise in the context of arbitrary heterophily, with the aim of rectifying noisy labels and assigning labels to previously unlabeled nodes.

Denoising Node Classification

Zone Evaluation: Revealing Spatial Bias in Object Detection

1 code implementation20 Oct 2023 Zhaohui Zheng, Yuming Chen, Qibin Hou, Xiang Li, Ping Wang, Ming-Ming Cheng

A fundamental limitation of object detectors is that they suffer from "spatial bias", and in particular perform less satisfactorily when detecting objects near image borders.

object-detection Object Detection

Uncertainty-aware Parameter-Efficient Self-training for Semi-supervised Language Understanding

1 code implementation19 Oct 2023 Jianing Wang, Qiushi Sun, Nuo Chen, Chengyu Wang, Jun Huang, Ming Gao, Xiang Li

The recent success of large pre-trained language models (PLMs) heavily hinges on massive labeled data, which typically produces inferior performance in low-resource scenarios.

Prompt Tuning for Multi-View Graph Contrastive Learning

no code implementations16 Oct 2023 Chenghua Gong, Xiang Li, Jianxiang Yu, Cheng Yao, Jiaqi Tan, Chengcheng Yu, Dawei Yin

Third, we design a prompting tuning method for our multi-view graph contrastive learning method to bridge the gap between pretexts and downsteam tasks.

Contrastive Learning

Empower Text-Attributed Graphs Learning with Large Language Models (LLMs)

no code implementations15 Oct 2023 Jianxiang Yu, Yuxiang Ren, Chenghua Gong, Jiaqi Tan, Xiang Li, Xuecang Zhang

In order to tackle this challenge, we propose a lightweight paradigm called ENG, which adopts a plug-and-play approach to empower text-attributed graphs through node generation using LLMs.

Few-Shot Learning Graph Learning +3

DropMix: Better Graph Contrastive Learning with Harder Negative Samples

1 code implementation15 Oct 2023 Yueqi Ma, Minjie Chen, Xiang Li

Recently, Mixup has been introduced to synthesize hard negative samples in graph contrastive learning (GCL).

Contrastive Learning

Context-aware Session-based Recommendation with Graph Neural Networks

1 code implementation14 Oct 2023 Zhihui Zhang, Jianxiang Yu, Xiang Li

Session-based recommendation (SBR) is a task that aims to predict items based on anonymous sequences of user behaviors in a session.

Session-Based Recommendations

MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning

1 code implementation14 Oct 2023 Jun Chen, Deyao Zhu, Xiaoqian Shen, Xiang Li, Zechun Liu, Pengchuan Zhang, Raghuraman Krishnamoorthi, Vikas Chandra, Yunyang Xiong, Mohamed Elhoseiny

Motivated by this, we target to build a unified interface for completing many vision-language tasks including image description, visual question answering, and visual grounding, among others.

Language Modelling Large Language Model +4

DialCoT Meets PPO: Decomposing and Exploring Reasoning Paths in Smaller Language Models

1 code implementation8 Oct 2023 Chengcheng Han, Xiaowei Du, Che Zhang, Yixin Lian, Xiang Li, Ming Gao, Baoyuan Wang

Chain-of-Thought (CoT) prompting has proven to be effective in enhancing the reasoning capabilities of Large Language Models (LLMs) with at least 100 billion parameters.

Arithmetic Reasoning

Uncertainty Quantification in Inverse Models in Hydrology

no code implementations3 Oct 2023 Somya Sharma Chatterjee, Rahul Ghosh, Arvind Renganathan, Xiang Li, Snigdhansu Chatterjee, John Nieber, Christopher Duffy, Vipin Kumar

Our inverse model offers 3\% improvement in R$^2$ for the inverse model (basin characteristic estimation) and 6\% for the forward model (streamflow prediction).

Completing Visual Objects via Bridging Generation and Segmentation

no code implementations1 Oct 2023 Xiang Li, Yinpeng Chen, Chung-Ching Lin, Rita Singh, Bhiksha Raj, Zicheng Liu

This paper presents a novel approach to object completion, with the primary goal of reconstructing a complete object from its partially visible components.

Image Generation Segmentation

Corex: Pushing the Boundaries of Complex Reasoning through Multi-Model Collaboration

1 code implementation30 Sep 2023 Qiushi Sun, Zhangyue Yin, Xiang Li, Zhiyong Wu, Xipeng Qiu, Lingpeng Kong

Large Language Models (LLMs) are evolving at an unprecedented pace and have exhibited considerable capability in the realm of natural language processing (NLP) with world knowledge.

World Knowledge

Rethinking Audiovisual Segmentation with Semantic Quantization and Decomposition

no code implementations29 Sep 2023 Xiang Li, Jinglu Wang, Xiaohao Xu, Xiulian Peng, Rita Singh, Yan Lu, Bhiksha Raj

Specifically, we propose a semantic decomposition method based on product quantization, where the multi-source semantics can be decomposed and represented by several quantized single-source semantics.


Generating Personalized Insulin Treatments Strategies with Deep Conditional Generative Time Series Models

no code implementations28 Sep 2023 Manuel Schürch, Xiang Li, Ahmed Allam, Giulia Rathmes, Amina Mollaysa, Claudia Cavelti-Weder, Michael Krauthammer

We propose a novel framework that combines deep generative time series models with decision theory for generating personalized treatment strategies.

Time Series

MedEdit: Model Editing for Medical Question Answering with External Knowledge Bases

no code implementations27 Sep 2023 Yucheng Shi, Shaochen Xu, Zhengliang Liu, Tianming Liu, Xiang Li, Ninghao Liu

Focusing on medical QA using the MedQA-SMILE dataset, we evaluate the impact of different retrieval models and the number of facts provided to the LLM.

Model Editing Question Answering +1

AutoPrep: An Automatic Preprocessing Framework for In-the-Wild Speech Data

no code implementations25 Sep 2023 Jianwei Yu, Hangting Chen, Yanyao Bian, Xiang Li, Yi Luo, Jinchuan Tian, Mengyang Liu, Jiayi Jiang, Shuai Wang

To address this issue, we introduce an automatic in-the-wild speech data preprocessing framework (AutoPrep) in this paper, which is designed to enhance speech quality, generate speaker labels, and produce transcriptions automatically.

Automatic Speech Recognition Speech Enhancement +3

MediViSTA-SAM: Zero-shot Medical Video Analysis with Spatio-temporal SAM Adaptation

1 code implementation24 Sep 2023 Sekeun Kim, Kyungsang Kim, Jiang Hu, Cheng Chen, Zhiliang Lyu, Ren Hui, Sunghwan Kim, Zhengliang Liu, Aoxiao Zhong, Xiang Li, Tianming Liu, Quanzheng Li

The Segmentation Anything Model (SAM) has attracted considerable attention as a foundational model well-known for its robust generalization capabilities across various downstream tasks.

Segmentation Video Segmentation +1

Asca: less audio data is more insightful

1 code implementation23 Sep 2023 Xiang Li, JunHao Chen, Chao Li, Hongwu Lv

Audio recognition in specialized areas such as birdsong and submarine acoustics faces challenges in large-scale pre-training due to the limitations in available samples imposed by sampling environments and specificity requirements.


Invisible Watermarking for Audio Generation Diffusion Models

2 code implementations22 Sep 2023 Xirong Cao, Xiang Li, Divyesh Jadav, Yanzhao Wu, Zhehui Chen, Chen Zeng, Wenqi Wei

Diffusion models have gained prominence in the image domain for their capabilities in data generation and transformation, achieving state-of-the-art performance in various tasks in both image and audio domains.

Audio Generation

A Discourse-level Multi-scale Prosodic Model for Fine-grained Emotion Analysis

no code implementations21 Sep 2023 Xianhao Wei, Jia Jia, Xiang Li, Zhiyong Wu, Ziyi Wang

More interestingly, although we aim at the synthesis effect of the style transfer model, the synthesized speech by the proposed text prosodic analysis model is even better than the style transfer from the original speech in some user evaluation indicators.

Emotion Recognition Speech Synthesis +1

MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation

1 code implementation16 Sep 2023 Cheng Chen, Juzheng Miao, Dufan Wu, Zhiling Yan, Sekeun Kim, Jiang Hu, Aoxiao Zhong, Zhengliang Liu, Lichao Sun, Xiang Li, Tianming Liu, Pheng-Ann Heng, Quanzheng Li

The Segment Anything Model (SAM), a foundation model for general image segmentation, has demonstrated impressive zero-shot performance across numerous natural image segmentation tasks.

Image Segmentation Medical Image Segmentation +4

SnakeGAN: A Universal Vocoder Leveraging DDSP Prior Knowledge and Periodic Inductive Bias

no code implementations14 Sep 2023 Sipan Li, Songxiang Liu, Luwen Zhang, Xiang Li, Yanyao Bian, Chao Weng, Zhiyong Wu, Helen Meng

However, it is still challenging to train a universal vocoder which can generalize well to out-of-domain (OOD) scenarios, such as unseen speaking styles, non-speech vocalization, singing, and musical pieces.

Inductive Bias

Quantifying and Attributing the Hallucination of Large Language Models via Association Analysis

no code implementations11 Sep 2023 Li Du, Yequan Wang, Xingrun Xing, Yiqun Ya, Xiang Li, Xin Jiang, Xuezhi Fang

Although demonstrating superb performance on various NLP tasks, large language models (LLMs) still suffer from the hallucination problem, which threatens the reliability of LLMs.

Instruction Following Memorization +1

FLM-101B: An Open LLM and How to Train It with $100K Budget

no code implementations7 Sep 2023 Xiang Li, Yiqun Yao, Xin Jiang, Xuezhi Fang, Xuying Meng, Siqi Fan, Peng Han, Jing Li, Li Du, Bowen Qin, Zheng Zhang, Aixin Sun, Yequan Wang

We demonstrate that a 101B-parameter LLM with 0. 31T tokens can be trained with a budget of 100K US dollars.


Exchanging-based Multimodal Fusion with Transformer

1 code implementation5 Sep 2023 Renyu Zhu, Chengcheng Han, Yong Qian, Qiushi Sun, Xiang Li, Ming Gao, Xuezhi Cao, Yunsen Xian

To solve these issues, in this paper, we propose a novel exchanging-based multimodal fusion model MuSE for text-vision fusion based on Transformer.

Image Captioning Multimodal Sentiment Analysis +2

Graph Self-Contrast Representation Learning

no code implementations5 Sep 2023 Minjie Chen, Yao Cheng, Ye Wang, Xiang Li, Ming Gao

Further, Since the triplet loss only optimizes the relative distance between the anchor and its positive/negative samples, it is difficult to ensure the absolute distance between the anchor and positive sample.

Contrastive Learning Graph Representation Learning +1

RigNet++: Efficient Repetitive Image Guided Network for Depth Completion

no code implementations1 Sep 2023 Zhiqiang Yan, Xiang Li, Zhenyu Zhang, Jun Li, Jian Yang

In the latter branch, we introduce a repetitive guidance module based on dynamic convolution, in which an efficient convolution factorization is proposed to reduce the complexity while modeling high-frequency structures progressively.

Depth Completion Depth Estimation +1

StratMed: Relevance Stratification between Biomedical Entities for Sparsity on Medication Recommendation

no code implementations31 Aug 2023 Xiang Li, Shunpan Liang, Yulei Hou, Tengfei Ma

After that, we design a pyramid-like stratification method based on relevance to strengthen the expressiveness of sparse data.

Listen to Minority: Encrypted Traffic Classification for Class Imbalance with Contrastive Pre-Training

no code implementations31 Aug 2023 Xiang Li, Juncheng Guo, Qige Song, Jiang Xie, Yafei Sang, Shuyuan Zhao, Yongzheng Zhang

Despite some existing learning-based ETC methods showing promising results, three-fold limitations still remain in real-world network environments, 1) label bias caused by traffic class imbalance, 2) traffic homogeneity caused by component sharing, and 3) training with reliance on sufficient labeled traffic.

Pseudo Label Traffic Classification

Rethinking Mobile AI Ecosystem in the LLM Era

no code implementations28 Aug 2023 Jinliang Yuan, Chen Yang, Dongqi Cai, Shihe Wang, Xin Yuan, Zeling Zhang, Xiang Li, Dingge Zhang, Hanzi Mei, Xianqing Jia, Shangguang Wang, Mengwei Xu

Concurrently, each app contributes a concise, offline fine-tuned "adapter" tailored to distinct downstream tasks.

Federated Fine-tuning of Billion-Sized Language Models across Mobile Devices

no code implementations26 Aug 2023 Mengwei Xu, Yaozong Wu, Dongqi Cai, Xiang Li, Shangguang Wang

Federated Learning (FL), a method to preserve user data privacy, is often employed in fine-tuning LLMs to downstream mobile tasks, an approach known as FedLLM.

Federated Learning

Decoding Natural Images from EEG for Object Recognition

no code implementations25 Aug 2023 Yonghao Song, Bingchuan Liu, Xiang Li, Nanlin Shi, Yijun Wang, Xiaorong Gao

This paper presents a self-supervised framework to demonstrate the feasibility of learning image representations from EEG signals, particularly for object recognition.

Contrastive Learning EEG +2

ADNet: Lane Shape Prediction via Anchor Decomposition

1 code implementation ICCV 2023 Lingyu Xiao, Xiang Li, Sen yang, Wankou Yang

In this paper, we revisit the limitations of anchor-based lane detection methods, which have predominantly focused on fixed anchors that stem from the edges of the image, disregarding their versatility and quality.

Lane Detection

Relation-Oriented: Toward Causal Knowledge-Aligned AGI

no code implementations31 Jul 2023 Jia Li, Xiang Li

Observation-Oriented paradigm currently dominates relationship learning models, including AI-based ones, which inherently do not account for relationships with temporally nonlinear effects.

Representation Learning

You Can Backdoor Personalized Federated Learning

1 code implementation29 Jul 2023 Tiandi Ye, Cen Chen, Yinggui Wang, Xiang Li, Ming Gao

The resistance of pFL methods with parameter decoupling is attributed to the heterogeneous classifiers between malicious clients and benign counterparts.

Backdoor Attack Meta-Learning +1

UPFL: Unsupervised Personalized Federated Learning towards New Clients

no code implementations29 Jul 2023 Tiandi Ye, Cen Chen, Yinggui Wang, Xiang Li, Ming Gao

To address this challenge, we extend the adaptive risk minimization technique into the unsupervised personalized federated learning setting and propose our method, FedTTA.

Knowledge Distillation Personalized Federated Learning

MUSE: Multi-View Contrastive Learning for Heterophilic Graphs

no code implementations29 Jul 2023 Mengyi Yuan, Minjie Chen, Xiang Li

Finally, an alternating training scheme is adopted to ensure that unsupervised node representation learning and information fusion controller can mutually reinforce each other.

Contrastive Learning Node Classification +2

RSGPT: A Remote Sensing Vision Language Model and Benchmark

1 code implementation28 Jul 2023 Yuan Hu, Jianlong Yuan, Congcong Wen, Xiaonan Lu, Xiang Li

This dataset consists of human-annotated captions and visual question-answer pairs, allowing for a comprehensive assessment of VLMs in the context of RS.

Image Captioning Language Modelling

Rethinking Voice-Face Correlation: A Geometry View

no code implementations26 Jul 2023 Xiang Li, Yandong Wen, Muqiao Yang, Jinglu Wang, Rita Singh, Bhiksha Raj

Previous works on voice-face matching and voice-guided face synthesis demonstrate strong correlations between voice and face, but mainly rely on coarse semantic cues such as gender, age, and emotion.

3D Face Reconstruction Face Generation

Creative Birds: Self-Supervised Single-View 3D Style Transfer

2 code implementations ICCV 2023 Renke Wang, Guimin Que, Shuo Chen, Xiang Li, Jun Li, Jian Yang

Our focus lies primarily on birds, a popular subject in 3D reconstruction, for which no existing single-view 3D transfer methods have been developed. The method we propose seeks to generate a 3D mesh shape and texture of a bird from two single-view images.

3D Reconstruction Style Transfer

CohortGPT: An Enhanced GPT for Participant Recruitment in Clinical Study

no code implementations21 Jul 2023 Zihan Guan, Zihao Wu, Zhengliang Liu, Dufan Wu, Hui Ren, Quanzheng Li, Xiang Li, Ninghao Liu

Participant recruitment based on unstructured medical texts such as clinical notes and radiology reports has been a challenging yet important task for the cohort establishment in clinical research.

Few-Shot Learning text-classification +1

SAMAug: Point Prompt Augmentation for Segment Anything Model

1 code implementation3 Jul 2023 Haixing Dai, Chong Ma, Zhengliang Liu, Yiwei Li, Peng Shu, Xiaozheng Wei, Lin Zhao, Zihao Wu, Fang Zeng, Dajiang Zhu, Wei Liu, Quanzheng Li, Tianming Liu, Xiang Li

Starting with an initial point prompt, SAM produces an initial mask, which is then fed into our proposed SAMAug to generate augmented point prompts.

Image Segmentation Prompt Engineering +2

Review of Large Vision Models and Visual Prompt Engineering

no code implementations3 Jul 2023 Jiaqi Wang, Zhengliang Liu, Lin Zhao, Zihao Wu, Chong Ma, Sigang Yu, Haixing Dai, Qiushi Yang, Yiheng Liu, Songyao Zhang, Enze Shi, Yi Pan, Tuo Zhang, Dajiang Zhu, Xiang Li, Xi Jiang, Bao Ge, Yixuan Yuan, Dinggang Shen, Tianming Liu, Shu Zhang

This review aims to summarize the methods employed in the computer vision domain for large vision models and visual prompt engineering, exploring the latest advancements in visual prompt engineering.

Prompt Engineering

Higher-order Graph Attention Network for Stock Selection with Joint Analysis

no code implementations27 Jun 2023 Yang Qiao, Yiping Xia, Xiang Li, Zheng Li, Yan Ge

H-GAT is able to capture higher-order structures and jointly incorporate factors of fundamental analysis with factors of technical analysis.

Graph Attention Stock Prediction

Privacy-Preserving Community Detection for Locally Distributed Multiple Networks

no code implementations27 Jun 2023 Xiao Guo, Xiang Li, Xiangyu Chang, Shujie Ma

To remove the bias incurred by RR and the squared network matrices, we develop a two-step bias-adjustment procedure.

Clustering Community Detection +2

Segment Anything Model (SAM) for Radiation Oncology

no code implementations20 Jun 2023 Lian Zhang, Zhengliang Liu, Lu Zhang, Zihao Wu, Xiaowei Yu, Jason Holmes, Hongying Feng, Haixing Dai, Xiang Li, Quanzheng Li, Dajiang Zhu, Tianming Liu, Wei Liu

Given that SAM, a model pre-trained purely on natural images, can handle the delineation of OARs from medical images with clinically acceptable accuracy, these results highlight SAM's robust generalization capabilities with consistent accuracy in automatic segmentation for radiotherapy.


CrossKD: Cross-Head Knowledge Distillation for Dense Object Detection

1 code implementation20 Jun 2023 Jiabao Wang, Yuming Chen, Zhaohui Zheng, Xiang Li, Ming-Ming Cheng, Qibin Hou

Such a distillation manner relieves the student's head from receiving contradictory supervision signals from the ground-truth annotations and the teacher's predictions, greatly improving the student's detection performance.

Dense Object Detection Knowledge Distillation +2

AD-AutoGPT: An Autonomous GPT for Alzheimer's Disease Infodemiology

no code implementations16 Jun 2023 Haixing Dai, Yiwei Li, Zhengliang Liu, Lin Zhao, Zihao Wu, Suhang Song, Ye Shen, Dajiang Zhu, Xiang Li, Sheng Li, Xiaobai Yao, Lu Shi, Quanzheng Li, Zhuo Chen, Donglan Zhang, Gengchen Mai, Tianming Liu

In this pioneering study, inspired by AutoGPT, the state-of-the-art open-source application based on the GPT-4 large language model, we develop a novel tool called AD-AutoGPT which can conduct data collection, processing, and analysis about complex health narratives of Alzheimer's Disease in an autonomous manner via users' textual prompts.

Language Modelling Large Language Model

Network Robustness Learning via Graph Transformer

no code implementations12 Jun 2023 Yu Zhang, Jia Li, Jie Ding, Xiang Li

Learning and analysis of network robustness, including controllability robustness and connectivity robustness, is critical for various networked systems against attacks.

Shapley Value on Probabilistic Classifiers

no code implementations12 Jun 2023 Xiang Li, Haocheng Xia, Jinfei Liu

Data valuation has become an increasingly significant discipline in data science due to the economic value of data.

Data Valuation

Boosting Language Models Reasoning with Chain-of-Knowledge Prompting

no code implementations10 Jun 2023 Jianing Wang, Qiushi Sun, Nuo Chen, Xiang Li, Ming Gao

To mitigate this brittleness, we propose a novel Chain-of-Knowledge (CoK) prompting, where we aim at eliciting LLMs to generate explicit pieces of knowledge evidence in the form of structure triple.

Arithmetic Reasoning

Artificial General Intelligence for Medical Imaging

no code implementations8 Jun 2023 Xiang Li, Lu Zhang, Zihao Wu, Zhengliang Liu, Lin Zhao, Yixuan Yuan, Jun Liu, Gang Li, Dajiang Zhu, Pingkun Yan, Quanzheng Li, Wei Liu, Tianming Liu, Dinggang Shen

In this review, we explore the potential applications of Artificial General Intelligence (AGI) models in healthcare, focusing on foundational Large Language Models (LLMs), Large Vision Models, and Large Multimodal Models.

Variable Radiance Field for Real-Life Category-Specifc Reconstruction from Single Image

no code implementations8 Jun 2023 Kun Wang, Zhiqiang Yan, Zhenyu Zhang, Xiang Li, Jun Li, Jian Yang

Our key contributions are: (1) We parameterize the geometry and appearance of the object using a multi-scale global feature extractor, which avoids frequent point-wise feature retrieval and camera dependency.

Contrastive Learning Retrieval

Fine-Grained Visual Prompting

no code implementations NeurIPS 2023 Lingfeng Yang, Yueze Wang, Xiang Li, Xinlong Wang, Jian Yang

Vision-Language Models (VLMs), such as CLIP, have demonstrated impressive zero-shot transfer capabilities in image-level visual perception.

Visual Prompting

Modeling Dual Period-Varying Preferences for Takeaway Recommendation

1 code implementation7 Jun 2023 Yuting Zhang, Yiqing Wu, Ran Le, Yongchun Zhu, Fuzhen Zhuang, Ruidong Han, Xiang Li, Wei Lin, Zhulin An, Yongjun Xu

Different from traditional recommendation, takeaway recommendation faces two main challenges: (1) Dual Interaction-Aware Preference Modeling.

Recommendation Systems

Exploring Better Text Image Translation with Multimodal Codebook

1 code implementation27 May 2023 Zhibin Lan, Jiawei Yu, Xiang Li, Wen Zhang, Jian Luan, Bin Wang, Degen Huang, Jinsong Su

Text image translation (TIT) aims to translate the source texts embedded in the image to target translations, which has a wide range of applications and thus has important research value.

Machine Translation Optical Character Recognition +2

BiomedGPT: A Unified and Generalist Biomedical Generative Pre-trained Transformer for Vision, Language, and Multimodal Tasks

1 code implementation26 May 2023 Kai Zhang, Jun Yu, Zhiling Yan, Yixin Liu, Eashan Adhikarla, Sunyang Fu, Xun Chen, Chen Chen, Yuyin Zhou, Xiang Li, Lifang He, Brian D. Davison, Quanzheng Li, Yong Chen, Hongfang Liu, Lichao Sun

In this paper, we introduce a unified and generalist Biomedical Generative Pre-trained Transformer (BiomedGPT) model, which leverages self-supervision on large and diverse datasets to accept multi-modal inputs and perform a range of downstream tasks.

Image Captioning Medical Visual Question Answering +2

TransCoder: Towards Unified Transferable Code Representation Learning Inspired by Human Skills

no code implementations23 May 2023 Qiushi Sun, Nuo Chen, Jianing Wang, Xiang Li, Ming Gao

To tackle the issue, in this paper, we present TransCoder, a unified Transferable fine-tuning strategy for Code representation learning.

Clone Detection Code Summarization +2

Is Synthetic Data From Diffusion Models Ready for Knowledge Distillation?

1 code implementation22 May 2023 Zheng Li, YuXuan Li, Penghai Zhao, RenJie Song, Xiang Li, Jian Yang

Diffusion models have recently achieved astonishing performance in generating high-fidelity photo-realistic images.

Knowledge Distillation

S$^3$HQA: A Three-Stage Approach for Multi-hop Text-Table Hybrid Question Answering

1 code implementation19 May 2023 Fangyu Lei, Xiang Li, Yifan Wei, Shizhu He, Yiming Huang, Jun Zhao, Kang Liu

In this paper, we propose a three-stage TextTableQA framework S3HQA, which comprises of retriever, selector, and reasoner.

Question Answering Reading Comprehension

SPP-CNN: An Efficient Framework for Network Robustness Prediction

no code implementations13 May 2023 Chengpei Wu, Yang Lou, Lin Wang, Junli Li, Xiang Li, Guanrong Chen

This paper addresses the robustness of a network to sustain its connectivity and controllability against malicious attacks.

Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation

1 code implementation10 May 2023 Di Jin, Luzhi Wang, Yizhen Zheng, Guojie Song, Fei Jiang, Xiang Li, Wei Lin, Shirui Pan

We design a dual-intent network to learn user intent from an attention mechanism and the distribution of historical data respectively, which can simulate users' decision-making process in interacting with a new item.

Decision Making Session-Based Recommendations +1

Vision-Language Models in Remote Sensing: Current Progress and Future Trends

2 code implementations9 May 2023 Congcong Wen, Yuan Hu, Xiang Li, Zhenghang Yuan, Xiao Xiang Zhu

This makes them better suited for tasks that require both visual and textual understanding, such as image captioning, text-based image retrieval, and visual question answering.

Image Captioning Image Generation +8

ChatGraph: Interpretable Text Classification by Converting ChatGPT Knowledge to Graphs

1 code implementation3 May 2023 Yucheng Shi, Hehuan Ma, Wenliang Zhong, Qiaoyu Tan, Gengchen Mai, Xiang Li, Tianming Liu, Junzhou Huang

To tackle these limitations, we propose a novel framework that leverages the power of ChatGPT for specific tasks, such as text classification, while improving its interpretability.

Decision Making Language Modelling +3

FreeLM: Fine-Tuning-Free Language Model

no code implementations2 May 2023 Xiang Li, Xin Jiang, Xuying Meng, Aixin Sun, Yequan Wang

FreeLM outperforms large models e. g., GPT-3 and InstructGPT, on a range of language understanding tasks in experiments.

Language Modelling

Instruction-ViT: Multi-Modal Prompts for Instruction Learning in ViT

no code implementations29 Apr 2023 Zhenxiang Xiao, Yuzhong Chen, Lu Zhang, Junjie Yao, Zihao Wu, Xiaowei Yu, Yi Pan, Lin Zhao, Chong Ma, Xinyu Liu, Wei Liu, Xiang Li, Yixuan Yuan, Dinggang Shen, Dajiang Zhu, Tianming Liu, Xi Jiang

Prompts have been proven to play a crucial role in large language models, and in recent years, vision models have also been using prompts to improve scalability for multiple downstream tasks.

Image Classification

Asymptotic Behaviors and Phase Transitions in Projected Stochastic Approximation: A Jump Diffusion Approach

no code implementations25 Apr 2023 Jiadong Liang, Yuze Han, Xiang Li, Zhihua Zhang

Additionally, we propose the Debiased LPSA (DLPSA) as a practical application of our jump diffusion approximation result.

Differentiate ChatGPT-generated and Human-written Medical Texts

no code implementations23 Apr 2023 Wenxiong Liao, Zhengliang Liu, Haixing Dai, Shaochen Xu, Zihao Wu, Yiyang Zhang, Xiaoke Huang, Dajiang Zhu, Hongmin Cai, Tianming Liu, Xiang Li

We focus on analyzing the differences between medical texts written by human experts and generated by ChatGPT, and designing machine learning workflows to effectively detect and differentiate medical texts generated by ChatGPT.

ChatABL: Abductive Learning via Natural Language Interaction with ChatGPT

no code implementations21 Apr 2023 Tianyang Zhong, Yaonai Wei, Li Yang, Zihao Wu, Zhengliang Liu, Xiaozheng Wei, Wenjun Li, Junjie Yao, Chong Ma, Xiang Li, Dajiang Zhu, Xi Jiang, Junwei Han, Dinggang Shen, Tianming Liu, Tuo Zhang

The proposed method uses the strengths of LLMs' understanding and logical reasoning to correct the incomplete logical facts for optimizing the performance of perceptual module, by summarizing and reorganizing reasoning rules represented in natural language format.

Decipherment Logical Reasoning

MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

3 code implementations20 Apr 2023 Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, Mohamed Elhoseiny

Our work, for the first time, uncovers that properly aligning the visual features with an advanced large language model can possess numerous advanced multi-modal abilities demonstrated by GPT-4, such as detailed image description generation and website creation from hand-drawn drafts.

Language Modelling Large Language Model +1

Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task

no code implementations18 Apr 2023 Zihao Wu, Lu Zhang, Chao Cao, Xiaowei Yu, Haixing Dai, Chong Ma, Zhengliang Liu, Lin Zhao, Gang Li, Wei Liu, Quanzheng Li, Dinggang Shen, Xiang Li, Dajiang Zhu, Tianming Liu

To this end, in this study, we evaluate the performance of ChatGPT/GPT-4 on a radiology NLI task and compare it to other models fine-tuned specifically on task-related data samples.

Specificity Task 2

ImpressionGPT: An Iterative Optimizing Framework for Radiology Report Summarization with ChatGPT

2 code implementations17 Apr 2023 Chong Ma, Zihao Wu, Jiaqi Wang, Shaochen Xu, Yaonai Wei, Zhengliang Liu, Xi Jiang, Lei Guo, Xiaoyan Cai, Shu Zhang, Tuo Zhang, Dajiang Zhu, Dinggang Shen, Tianming Liu, Xiang Li

The 'Impression' section of a radiology report is a critical basis for communication between radiologists and other physicians, and it is typically written by radiologists based on the 'Findings' section.

Video ChatCaptioner: Towards Enriched Spatiotemporal Descriptions

1 code implementation9 Apr 2023 Jun Chen, Deyao Zhu, Kilichbek Haydarov, Xiang Li, Mohamed Elhoseiny

Video captioning aims to convey dynamic scenes from videos using natural language, facilitating the understanding of spatiotemporal information within our environment.

Video Captioning

MoStGAN-V: Video Generation with Temporal Motion Styles

1 code implementation CVPR 2023 Xiaoqian Shen, Xiang Li, Mohamed Elhoseiny

Video generation remains a challenging task due to spatiotemporal complexity and the requirement of synthesizing diverse motions with temporal consistency.

Video Generation

Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models

no code implementations4 Apr 2023 Yiheng Liu, Tianle Han, Siyuan Ma, Jiayue Zhang, Yuanyuan Yang, Jiaming Tian, Hao He, Antong Li, Mengshen He, Zhengliang Liu, Zihao Wu, Lin Zhao, Dajiang Zhu, Xiang Li, Ning Qiang, Dingang Shen, Tianming Liu, Bao Ge

This paper presents a comprehensive survey of ChatGPT-related (GPT-3. 5 and GPT-4) research, state-of-the-art large language models (LLM) from the GPT series, and their prospective applications across diverse domains.

When Brain-inspired AI Meets AGI

no code implementations28 Mar 2023 Lin Zhao, Lu Zhang, Zihao Wu, Yuzhong Chen, Haixing Dai, Xiaowei Yu, Zhengliang Liu, Tuo Zhang, Xintao Hu, Xi Jiang, Xiang Li, Dajiang Zhu, Dinggang Shen, Tianming Liu

Artificial General Intelligence (AGI) has been a long-standing goal of humanity, with the aim of creating machines capable of performing any intellectual task that humans can do.

On de novo Bridging Paired-end RNA-seq Data

1 code implementation27 Mar 2023 Xiang Li, Mingfu Shao

Methods have been proposed to bridge paired-end reads in the presence of reference genome (called reference-based bridging), but the algorithms are far away from scaling for de novo bridging as the underlying compacted de Bruijn graph(cdBG) used in the latter task often contains millions of vertices and edges.

A Survey of Historical Learning: Learning Models with Learning History

1 code implementation23 Mar 2023 Xiang Li, Ge Wu, Lingfeng Yang, Wenhai Wang, RenJie Song, Jian Yang

The various types of elements, deposited in the training history, are a large amount of wealth for improving learning deep models.

Ensemble Learning

DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4

1 code implementation20 Mar 2023 Zhengliang Liu, Xiaowei Yu, Lu Zhang, Zihao Wu, Chao Cao, Haixing Dai, Lin Zhao, Wei Liu, Dinggang Shen, Quanzheng Li, Tianming Liu, Dajiang Zhu, Xiang Li

The digitization of healthcare has facilitated the sharing and re-using of medical data but has also raised concerns about confidentiality and privacy.

Benchmarking De-identification +3

Large Selective Kernel Network for Remote Sensing Object Detection

1 code implementation ICCV 2023 YuXuan Li, Qibin Hou, Zhaohui Zheng, Ming-Ming Cheng, Jian Yang, Xiang Li

To the best of our knowledge, this is the first time that large and selective kernel mechanisms have been explored in the field of remote sensing object detection.

object-detection Object Detection In Aerial Images +2

Digital staining in optical microscopy using deep learning -- a review

no code implementations14 Mar 2023 Lucas Kreiss, Shaowei Jiang, Xiang Li, Shiqi Xu, Kevin C. Zhou, Alexander Mühlberg, Kyung Chul Lee, Kanghyun Kim, Amey Chaware, Michael Ando, Laura Barisoni, Seung Ah Lee, Guoan Zheng, Kyle Lafata, Oliver Friedrich, Roarke Horstmeyer

Until recently, conventional biochemical staining had the undisputed status as well-established benchmark for most biomedical problems related to clinical diagnostics, fundamental research and biotechnology.


Sufficient Control of Complex Networks

no code implementations10 Mar 2023 Xiang Li, Guoqi Li, Leitao Gao, Beibei Li, Gaoxi Xiao

In this paper, we propose to study on sufficient control of complex networks which is to control a sufficiently large portion of the network, where only the quantity of controllable nodes matters.

Variance-aware robust reinforcement learning with linear function approximation under heavy-tailed rewards

no code implementations9 Mar 2023 Xiang Li, Qiang Sun

Building upon AdaOFUL, we propose VARA for linear MDPs, which achieves a tighter variance-aware regret bound of $\widetilde{O}(d\sqrt{HG^*K})$.

Decision Making regression +2

Non-aligned supervision for Real Image Dehazing

no code implementations8 Mar 2023 Junkai Fan, Fei Guo, Jianjun Qian, Xiang Li, Jun Li, Jian Yang

In particular, we explore a non-alignment setting by utilizing a clear reference image that is not aligned with the hazy input image to supervise the dehazing network through a multi-scale reference loss that compares the features of the two images.

Image Dehazing

Rethinking the Reasonability of the Test Set for Simultaneous Machine Translation

1 code implementation2 Mar 2023 Mengge Liu, Wen Zhang, Xiang Li, Jian Luan, Bin Wang, Yuhang Guo, Shuoying Chen

Simultaneous machine translation (SimulMT) models start translation before the end of the source sentence, making the translation monotonically aligned with the source sentence.

Machine Translation Test +1

HopFIR: Hop-wise GraphFormer with Intragroup Joint Refinement for 3D Human Pose Estimation

no code implementations ICCV 2023 Kai Zhai, Qiang Nie, Bo Ouyang, Xiang Li, Shanlin Yang

The HGF module groups the joints by k-hop neighbors and applies a hopwise transformer-like attention mechanism to these groups to discover latent joint synergies.

3D Human Pose Estimation

Pushing One Pair of Labels Apart Each Time in Multi-Label Learning: From Single Positive to Full Labels

no code implementations28 Feb 2023 Xiang Li, Xinrui Wang, Songcan Chen

In Multi-Label Learning (MLL), it is extremely challenging to accurately annotate every appearing object due to expensive costs and limited knowledge.

Multi-Label Learning

GradMA: A Gradient-Memory-based Accelerated Federated Learning with Alleviated Catastrophic Forgetting

1 code implementation CVPR 2023 Kangyang Luo, Xiang Li, Yunshi Lan, Ming Gao

Federated Learning (FL) has emerged as a de facto machine learning area and received rapid increasing research interests from the community.

Continual Learning Federated Learning +1

Statistical Analysis of Karcher Means for Random Restricted PSD Matrices

no code implementations24 Feb 2023 Hengchao Chen, Xiang Li, Qiang Sun

Non-asymptotic statistical analysis is often missing for modern geometry-aware machine learning algorithms due to the possibly intricate non-linear manifold structure.

Online Statistical Inference for Nonlinear Stochastic Approximation with Markovian Data

no code implementations15 Feb 2023 Xiang Li, Jiadong Liang, Zhihua Zhang

We study the statistical inference of nonlinear stochastic approximation algorithms utilizing a single trajectory of Markovian data.

Q-Learning valid

Meta-Learning Triplet Network with Adaptive Margins for Few-Shot Named Entity Recognition

1 code implementation14 Feb 2023 Chengcheng Han, Renyu Zhu, Jun Kuang, FengJiao Chen, Xiang Li, Ming Gao, Xuezhi Cao, Wei Wu

We design an improved triplet network to map samples and prototype vectors into a low-dimensional space that is easier to be classified and propose an adaptive margin for each entity type.

few-shot-ner Few-shot NER +5

Meta-Learning Siamese Network for Few-Shot Text Classification

1 code implementation5 Feb 2023 Chengcheng Han, Yuhe Wang, Yingnan Fu, Xiang Li, Minghui Qiu, Ming Gao, Aoying Zhou

Few-shot learning has been used to tackle the problem of label scarcity in text classification, of which meta-learning based methods have shown to be effective, such as the prototypical networks (PROTO).

Descriptive Few-Shot Learning +3

Structure Flow-Guided Network for Real Depth Super-Resolution

no code implementations31 Jan 2023 Jiayi Yuan, Haobo Jiang, Xiang Li, Jianjun Qian, Jun Li, Jian Yang

Specifically, our framework consists of a cross-modality flow-guided upsampling network (CFUNet) and a flow-enhanced pyramid edge attention network (PEANet).

Depth Estimation Depth Prediction +1

Recurrent Structure Attention Guidance for Depth Super-Resolution

no code implementations31 Jan 2023 Jiayi Yuan, Haobo Jiang, Xiang Li, Jianjun Qian, Jun Li, Jian Yang

Second, instead of the coarse concatenation guidance, we propose a recurrent structure attention block, which iteratively utilizes the latest depth estimation and the image features to jointly select clear patterns and boundaries, aiming at providing refined guidance for accurate depth recovery.

Depth Estimation Super-Resolution

SeeGera: Self-supervised Semi-implicit Graph Variational Auto-encoders with Masking

no code implementations29 Jan 2023 Xiang Li, Tiandi Ye, Caihua Shan, Dongsheng Li, Ming Gao

In this paper, to comprehensively enhance the performance of generative graph SSL against other GCL models on both unsupervised and supervised learning tasks, we propose the SeeGera model, which is based on the family of self-supervised variational graph auto-encoder (VGAE).

Contrastive Learning Self-Supervised Learning +1

Towards Spatial Equilibrium Object Detection

1 code implementation14 Jan 2023 Zhaohui Zheng, Yuming Chen, Qibin Hou, Xiang Li, Ming-Ming Cheng

In this paper, we study the spatial disequilibrium problem of modern object detectors and propose to quantify this ``spatial bias'' by measuring the detection performance over zones.

object-detection Object Detection

FishNet: A Large-scale Dataset and Benchmark for Fish Recognition, Detection, and Functional Trait Prediction

no code implementations ICCV 2023 Faizan Farooq Khan, Xiang Li, Andrew J. Temple, Mohamed Elhoseiny

Aquatic species are essential components of the world's ecosystem, and the preservation of aquatic biodiversity is crucial for maintaining proper ecosystem functioning.

Fish Detection