bluuebunny/crossref_metadata_2025 huggingface dataset:
Literacy and STEM Teachers Adapt AI Ethics Curriculum
Authors: Walsh Benjamin , Dalton Bridget , Forsyth Stacey , Yeh Tom
Published: June 2023

This article examines the ways secondary computer science and English Language Arts teachers in urban, suburban, and semi-rural schools adapted a project-based AI ethics curriculum to make it better fit their local contexts. AI ethics is an urgent topic with tangible consequences for youths’ current and future lives, but one that is rarely taught in schools. Few teachers have formal training in this area as it is an emerging field even at the university level. Exploring AI ethics involves examining biases related to race, gender, and social class, a challenging task for all teachers, and an unfamiliar one for most computer science teachers. It also requires teaching technical content which falls outside the comfort zone of most humanities teachers. Although none of our partner teachers had previously taught an AI ethics project, this study demonstrates that their expertise and experience in other domains played an essential role in providing high quality instruction. Teachers designed and redesigned tasks and incorporated texts and apps to ensure the AI ethics project would adhere to district and department level requirements; they led equity-focused inquiry in a way that both protected vulnerable students and accounted for local cultures and politics; and they adjusted technical content and developed hands-on computer science experiences to better challenge and engage their students. We use Mishra and Kohler’s TPACK framework to highlight the ways teachers leveraged their own expertise in some areas, while relying on materials and support from our research team in others, to create stronger learning experiences.
Music-to-Facial Expressions: Emotion-Based Music Visualization for the Hearing Impaired
Authors: Wang Yubo , Pan Fengzhou , Liu Danni , Hu Jiaxiong
Published: June 2023

While music is made to convey messages and emotions, auditory music is not equally accessible to everyone. Music visualization is a common approach to augment the listening experiences of the hearing users and to provide music experiences for the hearing-impaired. In this paper, we present a music visualization system that can turn the input of a piece of music into a series of facial expressions representative of the continuously changing sentiments in the music. The resulting facial expressions, recorded as action units, can later animate a static virtual avatar to be emotive synchronously with the music.
Theory of Mind: A Familiar Aspect of Humanity to Give Machines
Authors: Michelson Joel
Published: June 2023

My research focuses on machine models of theory of mind, a set of skills that helps humans cooperate with each other. Because these skills present themselves in behavior, inference-based measurements must be carefully designed to rule out alternate hypotheses. Producing models that display these skills requires an extensive understanding of experiences and mechanisms sufficient for learning, and the models must have robust generalization to be effective in varied domains. To address these problems, I intend to evaluate computational models of ToM using a variety of tests.
An Emotion-Guided Approach to Domain Adaptive Fake News Detection Using Adversarial Learning (Student Abstract)
Authors: Chakraborty Arkajyoti , Khatri Inder , Choudhry Arjun , Gupta Pankaj , Vishwakarma Dinesh Kumar , Prasad Mukesh
Published: June 2023

Recent works on fake news detection have shown the efficacy of using emotions as a feature for improved performance. However, the cross-domain impact of emotion-guided features for fake news detection still remains an open problem. In this work, we propose an emotion-guided, domain-adaptive, multi-task approach for cross-domain fake news detection, proving the efficacy of emotion-guided models in cross-domain settings for various datasets.
AsT: An Asymmetric-Sensitive Transformer for Osteonecrosis of the Femoral Head Detection (Student Abstract)
Authors: Chen Haoyang , Liu Shuai , Lu Feng , Li Wei , Sheng Bin , Li Mi , Jin Hai , Zomaya Albert Y.
Published: June 2023

Early diagnosis of osteonecrosis of the femoral head (ONFH) can inhibit the progression and improve femoral head preservation. The radiograph difference between early ONFH and healthy ones is not apparent to the naked eye. It is also hard to produce a large dataset to train the classification model. In this paper, we propose Asymmetric-Sensitive Transformer (AsT) to capture the uneven development of the bilateral femoral head to enable robust ONFH detection. Our ONFH detection is realized using the self-attention mechanism to femoral head regions while conferring sensitivity to the uneven development by the attention-shared transformer. The real-world experiment studies show that AsT achieves the best performance of AUC 0.9313 in the early diagnosis of ONFH and can find out misdiagnosis cases firmly.
Self-Paced Learning Based Graph Convolutional Neural Network for Mixed Integer Programming (Student Abstract)
Authors: Chen Li , Xu Hua , Wang Ziteng , Wang Chengming , Jiang Yu
Published: June 2023

Graph convolutional neural network (GCN) based methods have achieved noticeable performance in solving mixed integer programming problems (MIPs). However, the generalization of existing work is limited due to the problem structure. This paper proposes a self-paced learning (SPL) based GCN network (SPGCN) with curriculum learning (CL) to make the utmost of samples. SPGCN employs a GCN model to imitate the branching variable selection during the branch and bound process, while the training process is conducted in a self-paced fashion. Specifically, SPGCN contains a loss-based automatic difficulty measurer, where the training loss of the sample represents the difficulty level. In each iteration, a dynamic training dataset is constructed according to the difficulty level for GCN model training. Experiments on four NP-hard datasets verify that CL can lead to generalization improvement and convergence speedup in solving MIPs, where SPL performs better than predefined CL methods.
Disentangling the Benefits of Self-Supervised Learning to Deployment-Driven Downstream Tasks of Satellite Images (Student Abstract)
Authors: Deng Zhuo , Wei Yibing , Zhu Mingye , Wang Xueliang , Zhou Junchi , Yang Zhicheng , Zhou Hang , Cao Zhenjie , Ma Lan , Han Mei , Lai Jui-Hsin
Published: June 2023

In this paper, we investigate the benefits of self-supervised learning (SSL) to downstream tasks of satellite images. Unlike common student academic projects, this work focuses on the advantages of the SSL for deployment-driven tasks which have specific scenarios with low or high-spatial resolution images. Our preliminary experiments demonstrate the robust benefits of the SSL trained by medium-resolution (10m) images to both low-resolution (100m) scene classification case (4.25%↑) and very high-resolution (5cm) aerial image segmentation case (1.96%↑), respectively.
In-Game Toxic Language Detection: Shared Task and Attention Residuals (Student Abstract)
Authors: Jia Yuanzhe , Wu Weixuan , Cao Feiqi , Han Soyeon Caren
Published: June 2023

In-game toxic language becomes the hot potato in the gaming industry and community. There have been several online game toxicity analysis frameworks and models proposed. However, it is still challenging to detect toxicity due to the nature of in-game chat, which has extremely short length. In this paper, we describe how the in-game toxic language shared task has been established using the real-world in-game chat data. In addition, we propose and introduce the model/framework for toxic language token tagging (slot filling) from the in-game chat. The data and code will be released.
Mitigating Negative Transfer in Multi-Task Learning with Exponential Moving Average Loss Weighting Strategies (Student Abstract)
Authors: Lakkapragada Anish , Sleiman Essam , Surabhi Saimourya , Wall Dennis P.
Published: June 2023

Multi-Task Learning (MTL) is a growing subject of interest in deep learning, due to its ability to train models more efficiently on multiple tasks compared to using a group of conventional single-task models. However, MTL can be impractical as certain tasks can dominate training and hurt performance in others, thus making some tasks perform better in a single-task model compared to a multi-task one. Such problems are broadly classified as negative transfer, and many prior approaches in the literature have been made to mitigate these issues. One such current approach to alleviate negative transfer is to weight each of the losses so that they are on the same scale. Whereas current loss balancing approaches rely on either optimization or complex numerical analysis, none directly scale the losses based on their observed magnitudes. We propose multiple techniques for loss balancing based on scaling by the exponential moving average and benchmark them against current best-performing methods on three established datasets. On these datasets, they achieve comparable, if not higher, performance compared to current best-performing methods.
Double Policy Network for Aspect Sentiment Triplet Extraction (Student Abstract)
Authors: Li Xuting , Li Daifeng , Du Ruo , Chen Dingquan , Madden Andrew
Published: June 2023

Aspect Sentiment Triplet Extraction (ASTE) is the task to extract aspects, opinions and associated sentiments from sentences. Previous studies do not adequately consider the complicated interactions between aspect and opinion terms in both extraction logic and strategy. We present a novel Double Policy Network with Multi-Tag based Reward model (DPN-MTR), which adopts two networks ATE, TSOTE and a Trigger Mechanism to execute ASTE task following a more logical framework. A Multi-Tag based reward is also proposed to solve the limitations of existing studies for identifying aspect/opinion terms with multiple tokens (one term may consist of two or more tokens) to a certain extent. Extensive experiments are conducted on four widely-used benchmark datasets, and demonstrate the effectiveness of our model in generally improving the performance on ASTE significantly.
Learning Generalizable Batch Active Learning Strategies via Deep Q-networks (Student Abstract)
Authors: Li Yi-Chen , Shen Wen-Jie , Zhang Boyu , Mao Feng , Zhang Zongzhang , Yu Yang
Published: June 2023

To handle a large amount of unlabeled data, batch active learning (BAL) queries humans for the labels of a batch of the most valuable data points at every round. Most current BAL strategies are based on human-designed heuristics, such as uncertainty sampling or mutual information maximization. However, there exists a disagreement between these heuristics and the ultimate goal of BAL, i.e., optimizing the model's final performance within the query budgets. This disagreement leads to a limited generality of these heuristics. To this end, we formulate BAL as an MDP and propose a data-driven approach based on deep reinforcement learning. Our method learns the BAL strategy by maximizing the model's final performance. Experiments on the UCI benchmark show that our method can achieve competitive performance compared to existing heuristics-based approaches.
Can Graph Neural Networks Learn to Solve the MaxSAT Problem? (Student Abstract)
Authors: Liu Minghao , Huang Pei , Jia Fuqi , Zhang Fan , Sun Yuchen , Cai Shaowei , Ma Feifei , Zhang Jian
Published: June 2023

The paper presents an attempt to bridge the gap between machine learning and symbolic reasoning. We build graph neural networks (GNNs) to predict the solution of the Maximum Satisfiability (MaxSAT) problem, an optimization variant of SAT. Two closely related graph representations are adopted, and we prove their theoretical equivalence. We also show that GNNs can achieve attractive performance to solve hard MaxSAT problems in certain distributions even compared with state-of-the-art solvers through experimental evaluation.
Improving Adversarial Robustness to Sensitivity and Invariance Attacks with Deep Metric Learning (Student Abstract)
Authors: Ovalle Anaelia , Czyzycki Evan , Hsieh Cho-Jui
Published: June 2023

Intentionally crafted adversarial samples have effectively exploited weaknesses in deep neural networks. A standard method in adversarial robustness assumes a framework to defend against samples crafted by minimally perturbing a sample such that its corresponding model output changes. These sensitivity attacks exploit the model's sensitivity toward task-irrelevant features. Another form of adversarial sample can be crafted via invariance attacks, which exploit the model underestimating the importance of relevant features. Previous literature has indicated a tradeoff in defending against both attack types within a strictly L-p bounded defense. To promote robustness toward both types of attacks beyond Euclidean distance metrics, we use metric learning to frame adversarial regularization as an optimal transport problem. Our preliminary results indicate that regularizing over invariant perturbations in our framework improves both invariant and sensitivity defense.
Long Legal Article Question Answering via Cascaded Key Segment Learning (Student Abstract)
Authors: Xie Shugui , Li Lin , Yuan Jingling , Xie Qing , Tao Xiaohui
Published: June 2023

Current sentence-level evidence extraction based methods may lose the discourse coherence of legal articles since they tend to make the extracted sentences scattered over the article. To solve the problem, this paper proposes a Cascaded Answer-guided key segment learning framework for long Legal article Question Answering, namely CALQA. The framework consists of three cascaded modules: Sifter, Reader, and Responder. The Sifter transfers a long legal article into several segments and works in an answer-guided way by automatically sifting out key fact segments in a coarse-to-fine approach through multiple iterations. The Reader utilizes a set of attention mechanisms to obtain semantic representations of the question and key fact segments. Finally, considering it a multi-label classification task the Responder predicts final answers in a cascaded manner. CALQA outperforms state-of-the-art methods in CAIL 2021 Law dataset.
Clustered Federated Learning for Heterogeneous Data (Student Abstract)
Authors: Yu Xue , Liu Ziyi , Sun Yifan , Wang Wu
Published: June 2023

Federated Learning (FL) aims to achieve a global model via aggregating models from all devices. However, it can diverge when the data on the users’ devices are heterogeneous. To address this issue, we propose a novel clustered FL method (FPFC) based on a nonconvex pairwise fusion penalty. FPFC can automatically identify clusters without prior knowledge of the number of clusters and the set of devices in each cluster. Our method is implemented in parallel, updates only a subset of devices at each communication round, and allows each participating device to perform inexact computation. We also provide convergence guarantees of FPFC for general nonconvex losses. Experiment results demonstrate the advantages of FPFC over existing methods.
DyCVAE: Learning Dynamic Causal Factors for Non-stationary Series Domain Generalization (Student Abstract)
Authors: Zhang Weifeng , Wang Zhiyuan , Zhang Kunpeng , Zhong Ting , Zhou Fan
Published: June 2023

Learning domain-invariant representations is a major task of out-of-distribution generalization. To address this issue, recent efforts have taken into accounting causality, aiming at learning the causal factors with regard to tasks. However, extending existing generalization methods for adapting non-stationary time series may be ineffective, because they fail to model the underlying causal factors due to temporal-domain shifts except for source-domain shifts, as pointed out by recent studies. To this end, we propose a novel model DyCVAE to learn dynamic causal factors. The results on synthetic and real datasets demonstrate the effectiveness of our proposed model for the task of generalization in time series domain.
Exploiting High-Order Interaction Relations to Explore User Intent (Student Abstract)
Authors: Zheng Xiangping , Liang Xun , Wu Bo
Published: June 2023

This paper studies the problem of exploring the user intent for session-based recommendations. Its challenges come from the uncertainty of user behavior and limited information. However, current endeavors cannot fully explore the mutual interactions among sessions and do not explicitly model the complex high-order relations among items. To circumvent these critical issues, we innovatively propose a HyperGraph Convolutional Contrastive framework (termed HGCC) that consists of two crucial tasks: 1) The session-based recommendation (SBR task) that aims to capture the beyond pair-wise relationships between items and sessions. 2) The self-supervised learning (SSL task) acted as the auxiliary task to boost the former task. By jointly optimizing the two tasks, the performance of the recommendation task achieves decent gains. Experiments on multiple real-world datasets demonstrate the superiority of the proposed approach over the state-of-the-art methods.
Model-Based Offline Weighted Policy Optimization (Student Abstract)
Authors: Zhou Renzhe , Zhang Zongzhang , Yu Yang
Published: June 2023

A promising direction for applying reinforcement learning to the real world is learning from offline datasets. Offline reinforcement learning aims to learn policies from pre-collected datasets without online interaction with the environment. Due to the lack of further interaction, offline reinforcement learning faces severe extrapolation error, leading to policy learning failure. In this paper, we investigate the weighted Bellman update in model-based offline reinforcement learning. We explore uncertainty estimation in ensemble dynamics models, then use a variational autoencoder to fit the behavioral prior, and finally propose an algorithm called Model-Based Offline Weighted Policy Optimization (MOWPO), which uses a combination of model confidence and behavioral prior as weights to reduce the impact of inaccurate samples on policy optimization. Experiment results show that MOWPO achieves better performance than state-of-the-art algorithms, and both the model confidence weight and the behavioral prior weight can play an active role in offline policy optimization.
SOREO: A System for Safe and Autonomous Drones Fleet Navigation with Reinforcement Learning
Authors: Alami Reda , Hacid Hakim , Bellone Lorenzo , Barcis Michal , Natalizio Enrico
Published: June 2023

This demonstration introduces SOREO, a system that explores the possibility of extending UAVs autonomy through machine learning. It brings a contribution to the following problem: Having a fleet of drones and a geographic area, how to learn the shortest paths between any point with regards to the base points for optimal and safe package delivery?
Starting from a set of possible actions, a virtual design of a geographic location of interest, e.g., a city, and a reward value, SOREO is capable of learning not only how to prevent collisions with obstacles, e.g., walls and buildings, but also to find the shortest path between any two points, i.e., the base and the target. SOREO exploits based on the Q-learning algorithm.
Cross-Modality Earth Mover’s Distance for Visible Thermal Person Re-identification
Authors: Ling Yongguo , Zhong Zhun , Luo Zhiming , Yang Fengxiang , Cao Donglin , Lin Yaojin , Li Shaozi , Sebe Nicu
Published: June 2023

Visible thermal person re-identification (VT-ReID) suffers from inter-modality discrepancy and intra-identity variations. Distribution alignment is a popular solution for VT-ReID, however, it is usually restricted to the influence of the intra-identity variations. In this paper, we propose the Cross-Modality Earth Mover's Distance (CM-EMD) that can alleviate the impact of the intra-identity variations during modality alignment. CM-EMD selects an optimal transport strategy and assigns high weights to pairs that have a smaller intra-identity variation. In this manner, the model will focus on reducing the inter-modality discrepancy while paying less attention to intra-identity variations, leading to a more effective modality alignment. Moreover, we introduce two techniques to improve the advantage of CM-EMD. First, Cross-Modality Discrimination Learning (CM-DL) is designed to overcome the discrimination degradation problem caused by modality alignment. By reducing the ratio between intra-identity and inter-identity variances, CM-DL leads the model to learn more discriminative representations. Second, we construct the Multi-Granularity Structure (MGS), enabling us to align modalities from both coarse- and fine-grained levels with the proposed CM-EMD. Extensive experiments show the benefits of the proposed CM-EMD and its auxiliary techniques (CM-DL and MGS). Our method achieves state-of-the-art performance on two VT-ReID benchmarks.
Hypotheses Tree Building for One-Shot Temporal Sentence Localization
Authors: Liu Daizong , Fang Xiang , Zhou Pan , Di Xing , Lu Weining , Cheng Yu
Published: June 2023

Given an untrimmed video, temporal sentence localization (TSL) aims to localize a specific segment according to a given sentence query. Though respectable works have made decent achievements in this task, they severely rely on dense video frame annotations, which require a tremendous amount of human effort to collect. In this paper, we target another more practical and challenging setting: one-shot temporal sentence localization (one-shot TSL), which learns to retrieve the query information among the entire video with only one annotated frame. Particularly, we propose an effective and novel tree-structure baseline for one-shot TSL, called Multiple Hypotheses Segment Tree (MHST), to capture the query-aware discriminative frame-wise information under the insufficient annotations. Each video frame is taken as the leaf-node, and the adjacent frames sharing the same visual-linguistic semantics will be merged into the upper non-leaf node for tree building. At last, each root node is an individual segment hypothesis containing the consecutive frames of its leaf-nodes. During the tree construction, we also introduce a pruning strategy to eliminate the interference of query-irrelevant nodes. With our designed self-supervised loss functions, our MHST is able to generate high-quality segment hypotheses for ranking and selection with the query. Experiments on two challenging datasets demonstrate that MHST achieves competitive performance compared to existing methods.
From Coarse to Fine: Hierarchical Pixel Integration for Lightweight Image Super-resolution
Authors: Liu Jie , Chen Chao , Tang Jie , Wu Gangshan
Published: June 2023

Image super-resolution (SR) serves as a fundamental tool for the processing and transmission of multimedia data. Recently, Transformer-based models have achieved competitive performances in image SR. They divide images into fixed-size patches and apply self-attention on these patches to model long-range dependencies among pixels. However, this architecture design is originated for high-level vision tasks, which lacks design guideline from SR knowledge. In this paper, we aim to design a new attention block whose insights are from the interpretation of Local Attribution Map (LAM) for SR networks. Specifically, LAM presents a hierarchical importance map where the most important pixels are located in a fine area of a patch and some less important pixels are spread in a coarse area of the whole image. To access pixels in the coarse area, instead of using a very large patch size, we propose a lightweight Global Pixel Access (GPA) module that applies cross-attention with the most similar patch in an image. In the fine area, we use an Intra-Patch Self-Attention (IPSA) module to model long-range pixel dependencies in a local patch, and then a spatial convolution is applied to process the finest details. In addition, a Cascaded Patch Division (CPD) strategy is proposed to enhance perceptual quality of recovered images. Extensive experiments suggest that our method outperforms state-of-the-art lightweight SR methods by a large margin. Code is available at https://github.com/passerer/HPINet.
EMEF: Ensemble Multi-Exposure Image Fusion
Authors: Liu Renshuai , Li Chengyang , Cao Haitao , Zheng Yinglin , Zeng Ming , Cheng Xuan
Published: June 2023

Although remarkable progress has been made in recent years, current multi-exposure image fusion (MEF) research is still bounded by the lack of real ground truth, objective evaluation function, and robust fusion strategy. In this paper, we study the MEF problem from a new perspective. We don’t utilize any synthesized ground truth, design any loss function, or develop any fusion strategy. Our proposed method EMEF takes advantage of the wisdom of multiple imperfect MEF contributors including both conventional and deep learning-based methods. Specifically, EMEF consists of two main stages: pre-train an imitator network and tune the imitator in the runtime. In the first stage, we make a unified network imitate different MEF targets in a style modulation way. In the second stage, we tune the imitator network by optimizing the style code, in order to find an optimal fusion result for each input pair. In the experiment, we construct EMEF from four state-of-the-art MEF methods and then make comparisons with the individuals and several other competitive methods on the latest released MEF benchmark dataset. The promising experimental results demonstrate that our ensemble framework can “get the best of all worlds”. The code is available at https://github.com/medalwill/EMEF.
CCQ: Cross-Class Query Network for Partially Labeled Organ Segmentation
Authors: Liu Xuyang , Wen Bingbing , Yang Sibei
Published: June 2023

Learning multi-organ segmentation from multiple partially-labeled datasets attracts increasing attention. It can be a promising solution for the scarcity of large-scale, fully labeled 3D medical image segmentation datasets. However, existing algorithms of multi-organ segmentation on partially-labeled datasets neglect the semantic relations and anatomical priors between different categories of organs, which is crucial for partially-labeled multi-organ segmentation. In this paper, we tackle the limitations above by proposing the Cross-Class Query Network (CCQ). CCQ consists of an image encoder, a cross-class query learning module, and an attentive refinement segmentation module. More specifically, the image encoder captures the long-range dependency of a single image via the transformer encoder. Cross-class query learning module first generates query vectors that represent semantic concepts of different categories and then utilizes these query vectors to find the class-relevant features of image representation for segmentation. The attentive refinement segmentation module with an attentive skip connection incorporates the high-resolution image details and eliminates the class-irrelevant noise. Extensive experiment results demonstrate that CCQ outperforms all the state-of-the-art models on the MOTS dataset, which consists of seven organ and tumor segmentation tasks. Code is available at https://github.com/Yang-007/CCQ.git.
Self-Decoupling and Ensemble Distillation for Efficient Segmentation
Authors: Liu Yuang , Zhang Wei , Wang Jun
Published: June 2023

Knowledge distillation (KD) is a promising teacher-student learning paradigm that transfers information from a cumbersome teacher to a student network. To avoid the training cost of a large teacher network, the recent studies propose to distill knowledge from the student itself, called Self-KD. However, due to the limitations of the performance and capacity of the student, the soft-labels or features distilled by the student barely provide reliable guidance. Moreover, most of the Self-KD algorithms are specific to classification tasks based on soft-labels, and not suitable for semantic segmentation. To alleviate these contradictions, we revisit the label and feature distillation problem in segmentation, and propose Self-Decoupling and Ensemble Distillation for Efficient Segmentation (SDES). Specifically, we design a decoupled prediction ensemble distillation (DPED) algorithm that generates reliable soft-labels with multiple expert decoders, and a decoupled feature ensemble distillation (DFED) mechanism to utilize more important channel-wise feature maps for encoder learning. The extensive experiments on three public segmentation datasets demonstrate the superiority of our approach and the efficacy of each component in the framework through the ablation study.
GLT-T: Global-Local Transformer Voting for 3D Single Object Tracking in Point Clouds
Authors: Nie Jiahao , He Zhiwei , Yang Yuxiang , Gao Mingyu , Zhang Jing
Published: June 2023

Current 3D single object tracking methods are typically based on VoteNet, a 3D region proposal network. Despite the success, using a single seed point feature as the cue for offset learning in VoteNet prevents high-quality 3D proposals from being generated. Moreover, seed points with different importance are treated equally in the voting process, aggravating this defect. To address these issues, we propose a novel global-local transformer voting scheme to provide more informative cues and guide the model pay more attention on potential seed points, promoting the generation of high-quality 3D proposals. Technically, a global-local transformer (GLT) module is employed to integrate object- and patch-aware prior into seed point features to effectively form strong feature representation for geometric positions of the seed points, thus providing more robust and accurate cues for offset learning. Subsequently, a simple yet effective training strategy is designed to train the GLT module. We develop an importance prediction branch to learn the potential importance of the seed points and treat the output weights vector as a training constraint term. By incorporating the above components together, we exhibit a superior tracking method GLT-T. Extensive experiments on challenging KITTI and NuScenes benchmarks demonstrate that GLT-T achieves state-of-the-art performance in the 3D single object tracking task. Besides, further ablation studies show the advantages of the proposed global-local transformer voting scheme over the original VoteNet. Code and models will be available at https://github.com/haooozi/GLT-T.
Better and Faster: Adaptive Event Conversion for Event-Based Object Detection
Authors: Peng Yansong , Zhang Yueyi , Xiao Peilin , Sun Xiaoyan , Wu Feng
Published: June 2023

Event cameras are a kind of bio-inspired imaging sensor, which asynchronously collect sparse event streams with many advantages. In this paper, we focus on building better and faster event-based object detectors. To this end, we first propose a computationally efficient event representation Hyper Histogram, which adequately preserves both the polarity and temporal information of events. Then we devise an Adaptive Event Conversion module, which converts events into Hyper Histograms according to event density via an adaptive queue. Moreover, we introduce a novel event-based augmentation method Shadow Mosaic, which significantly improves the event sample diversity and enhances the generalization ability of detection models. We equip our proposed modules on three representative object detection models: YOLOv5, Deformable-DETR, and RetinaNet. Experimental results on three event-based detection datasets (1Mpx, Gen1, and MVSEC-NIGHTL21) demonstrate that our proposed approach outperforms other state-of-the-art methods by a large margin, while achieving a much faster running speed (< 14 ms and < 4 ms for 50 ms event data on the 1Mpx and Gen1 datasets).
Exploring Stochastic Autoregressive Image Modeling for Visual Representation
Authors: Qi Yu , Yang Fan , Zhu Yousong , Liu Yufei , Wu Liwei , Zhao Rui , Li Wei
Published: June 2023

Autoregressive language modeling (ALM) has been successfully used in self-supervised pre-training in Natural language processing (NLP). However, this paradigm has not achieved comparable results with other self-supervised approaches in computer vision (e.g., contrastive learning, masked image modeling). In this paper, we try to find the reason why autoregressive modeling does not work well on vision tasks. To tackle this problem, we fully analyze the limitation of visual autoregressive methods and proposed a novel stochastic autoregressive image modeling (named SAIM) by the two simple designs. First, we serialize the image into patches. Second, we employ the stochastic permutation strategy to generate an effective and robust image context which is critical for vision tasks. To realize this task, we create a parallel encoder-decoder training process in which the encoder serves a similar role to the standard vision transformer focusing on learning the whole contextual information, and meanwhile the decoder predicts the content of the current position so that the encoder and decoder can reinforce each other. Our method significantly improves the performance of autoregressive image modeling and achieves the best accuracy (83.9%) on the vanilla ViT-Base model among methods using only ImageNet-1K data. Transfer performance in downstream tasks also shows that our model achieves competitive performance. Code is available at https://github.com/qiy20/SAIM.
Channel Regeneration: Improving Channel Utilization for Compact DNNs
Authors: Sharma Ankit , Foroosh Hassan
Published: June 2023

Overparameterized deep neural networks have redundant neurons that do not contribute to the network's accuracy. In this paper, we introduce a novel channel regeneration technique that reinvigorates these redundant channels by re-initializing its batch normalization scaling factor gamma. This re-initialization of BN gamma promotes regular weight updates during training. Furthermore, we show that channel regeneration encourages the channels to contribute equally to the learned representation and further boosts the generalization accuracy. We apply our technique at regular intervals of the training cycle to improve channel utilization. The solutions proposed in previous works either raise the total computational cost or increase the model complexity. Integrating the proposed channel regeneration technique into the training methodology of efficient architectures requires minimal effort and comes at no additional cost in size or memory. Extensive experiments on several image classification and semantic segmentation benchmarks demonstrate the effectiveness of applying the channel regeneration technique to compact architectures.
YOLOV: Making Still Image Object Detectors Great at Video Object Detection
Authors: Shi Yuheng , Wang Naiyan , Guo Xiaojie
Published: June 2023

Video object detection (VID) is challenging because of the high variation of object appearance as well as the diverse deterioration in some frames. On the positive side, the detection in a certain frame of a video, compared with that in a still image, can draw support from other frames. Hence, how to aggregate features across different frames is pivotal to VID problem. Most of existing aggregation algorithms are customized for two-stage detectors. However, these detectors are usually computationally expensive due to their two-stage nature. This work proposes a simple yet effective strategy to address the above concerns, which costs marginal overheads with significant gains in accuracy. Concretely, different from traditional two-stage pipeline, we select important regions after the one-stage detection to avoid processing massive low-quality candidates. Besides, we evaluate the relationship between a target frame and reference frames to guide the aggregation. We conduct extensive experiments and ablation studies to verify the efficacy of our design, and reveal its superiority over other state-of-the-art VID approaches in both effectiveness and efficiency. Our YOLOX-based model can achieve promising performance (e.g., 87.5% AP50 at over 30 FPS on the ImageNet VID dataset on a single 2080Ti GPU), making it attractive for large-scale or real-time applications. The implementation is simple, we have made the demo codes and models available at https://github.com/YuHengsss/YOLOV.
Text-DIAE: A Self-Supervised Degradation Invariant Autoencoder for Text Recognition and Document Enhancement
Authors: Souibgui Mohamed Ali , Biswas Sanket , Mafla Andres , Biten Ali Furkan , Fornés Alicia , Kessentini Yousri , Lladós Josep , Gomez Lluis , Karatzas Dimosthenis
Published: June 2023

In this paper, we propose a Text-Degradation Invariant Auto Encoder (Text-DIAE), a self-supervised model designed to tackle two tasks, text recognition (handwritten or scene-text) and document image enhancement. We start by employing a transformer-based architecture that incorporates three pretext tasks as learning objectives to be optimized during pre-training without the usage of labelled data. Each of the pretext objectives is specifically tailored for the final downstream tasks. We conduct several ablation experiments that confirm the design choice of the selected pretext tasks. Importantly, the proposed model does not exhibit limitations of previous state-of-the-art methods based on contrastive losses, while at the same time requiring substantially fewer data samples to converge. Finally, we demonstrate that our method surpasses the state-of-the-art in existing supervised and self-supervised settings in handwritten and scene text recognition and document image enhancement. Our code and trained models will be made publicly available at https://github.com/dali92002/SSL-OCR
Referring Expression Comprehension Using Language Adaptive Inference
Authors: Su Wei , Miao Peihan , Dou Huanzhang , Fu Yongjian , Li Xi
Published: June 2023

Different from universal object detection, referring expression comprehension (REC) aims to locate specific objects referred to by natural language expressions. The expression provides high-level concepts of relevant visual and contextual patterns, which vary significantly with different expressions and account for only a few of those encoded in the REC model. This leads us to a question: do we really need the entire network with a fixed structure for various referring expressions? Ideally, given an expression, only expression-relevant components of the REC model are required. These components should be small in number as each expression only contains very few visual and contextual clues. This paper explores the adaptation between expressions and REC models for dynamic inference. Concretely, we propose a neat yet efficient framework named Language Adaptive Dynamic Subnets (LADS), which can extract language-adaptive subnets from the REC model conditioned on the referring expressions. By using the compact subnet, the inference can be more economical and efficient. Extensive experiments on RefCOCO, RefCOCO+, RefCOCOg, and Referit show that the proposed method achieves faster inference speed and higher accuracy against state-of-the-art approaches.
Deep Manifold Attack on Point Clouds via Parameter Plane Stretching
Authors: Tang Keke , Wu Jianpeng , Peng Weilong , Shi Yawen , Song Peng , Gu Zhaoquan , Tian Zhihong , Wang Wenping
Published: June 2023

Adversarial attack on point clouds plays a vital role in evaluating and improving the adversarial robustness of 3D deep learning models. Current attack methods are mainly applied by point perturbation in a non-manifold manner. In this paper, we formulate a novel manifold attack, which deforms the underlying 2-manifold surfaces via parameter plane stretching to generate adversarial point clouds. First, we represent the mapping between the parameter plane and underlying surface using generative-based networks. Second, the stretching is learned in the 2D parameter domain such that the generated 3D point cloud fools a pretrained classifier with minimal geometric distortion. Extensive experiments show that adversarial point clouds generated by manifold attack are smooth, undefendable and transferable, and outperform those samples generated by the state-of-the-art non-manifold ones.
Learning Context-Aware Classifier for Semantic Segmentation
Authors: Tian Zhuotao , Cui Jiequan , Jiang Li , Qi Xiaojuan , Lai Xin , Chen Yixin , Liu Shu , Jia Jiaya
Published: June 2023

Semantic segmentation is still a challenging task for parsing diverse contexts in different scenes, thus the fixed classifier might not be able to well address varying feature distributions during testing. Different from the mainstream literature where the efficacy of strong backbones and effective decoder heads has been well studied, in this paper, additional contextual hints are instead exploited via learning a context-aware classifier whose content is data-conditioned, decently adapting to different latent distributions. Since only the classifier is dynamically altered, our method is model-agnostic and can be easily applied to generic segmentation models. Notably, with only negligible additional parameters and +2\% inference time, decent performance gain has been achieved on both small and large models with challenging benchmarks, manifesting substantial practical merits brought by our simple yet effective method. The implementation is available at https://github.com/tianzhuotao/CAC.
LeNo: Adversarial Robust Salient Object Detection Networks with Learnable Noise
Authors: Wang He , Wan Lin , Tang He
Published: June 2023

Pixel-wise prediction with deep neural network has become an effective paradigm for salient object detection (SOD) and achieved remarkable performance. However, very few SOD models are robust against adversarial attacks which are visually imperceptible for human visual attention. The previous work robust saliency (ROSA) shuffles the pre-segmented superpixels and then refines the coarse saliency map by the densely connected conditional random field (CRF). Different from ROSA that rely on various pre- and post-processings, this paper proposes a light-weight Learnable Noise (LeNo) to defend adversarial attacks for SOD models. LeNo preserves accuracy of SOD models on both adversarial and clean images, as well as inference speed. In general, LeNo consists of a simple shallow noise and noise estimation that embedded in the encoder and decoder of arbitrary SOD networks respectively. Inspired by the center prior of human visual attention mechanism, we initialize the shallow noise with a cross-shaped gaussian distribution for better defense against adversarial attacks. Instead of adding additional network components for post-processing, the proposed noise estimation modifies only one channel of the decoder. With the deeply-supervised noise-decoupled training on state-of-the-art RGB and RGB-D SOD networks, LeNo outperforms previous works not only on adversarial images but also on clean images, which contributes stronger robustness for SOD. Our code is available at https://github.com/ssecv/LeNo.
Contrastive Masked Autoencoders for Self-Supervised Video Hashing
Authors: Wang Yuting , Wang Jinpeng , Chen Bin , Zeng Ziyun , Xia Shu-Tao
Published: June 2023

Self-Supervised Video Hashing (SSVH) models learn to generate short binary representations for videos without ground-truth supervision, facilitating large-scale video retrieval efficiency and attracting increasing research attention. The success of SSVH lies in the understanding of video content and the ability to capture the semantic relation among unlabeled videos. Typically, state-of-the-art SSVH methods consider these two points in a two-stage training pipeline, where they firstly train an auxiliary network by instance-wise mask-and-predict tasks and secondly train a hashing model to preserve the pseudo-neighborhood structure transferred from the auxiliary network. This consecutive training strategy is inflexible and also unnecessary. In this paper, we propose a simple yet effective one-stage SSVH method called ConMH, which incorporates video semantic information and video similarity relationship understanding in a single stage. To capture video semantic information for better hashing learning, we adopt an encoder-decoder structure to reconstruct the video from its temporal-masked frames. Particularly, we find that a higher masking ratio helps video understanding. Besides, we fully exploit the similarity relationship between videos by maximizing agreement between two augmented views of a video, which contributes to more discriminative and robust hash codes. Extensive experiments on three large-scale video datasets (i.e., FCVID, ActivityNet and YFCC) indicate that ConMH achieves state-of-the-art results. Code is available at https://github.com/huangmozhi9527/ConMH.
Scene Graph to Image Synthesis via Knowledge Consensus
Authors: Wu Yang , Wei Pengxu , Lin Liang
Published: June 2023

In this paper, we study graph-to-image generation conditioned exclusively on scene graphs, in which we seek to disentangle the veiled semantics between knowledge graphs and images. While most existing research resorts to laborious auxiliary information such as object layouts or segmentation masks, it is also of interest to unveil the generality of the model with limited supervision, moreover, avoiding extra cross-modal alignments. To tackle this challenge, we delve into the causality of the adversarial generation process, and reason out a new principle to realize a simultaneous semantic disentanglement with an alignment on target and model distributions. This principle is named knowledge consensus, which explicitly describes a triangle causal dependency among observed images, graph semantics and hidden visual representations. The consensus also determines a new graph-to-image generation framework, carried on several adversarial optimization objectives. Extensive experimental results demonstrate that, even conditioned only on scene graphs, our model surprisingly achieves superior performance on semantics-aware image generation, without losing the competence on manipulating the generation through knowledge graphs.
SVFI: Spiking-Based Video Frame Interpolation for High-Speed Motion
Authors: Xia Lujie , Zhao Jing , Xiong Ruiqin , Huang Tiejun
Published: June 2023

Occlusion and motion blur make it challenging to interpolate video frame, since estimating complex motions between two frames is hard and unreliable, especially in highly dynamic scenes. This paper aims to address these issues by exploiting spike stream as auxiliary visual information between frames to synthesize target frames. Instead of estimating motions by optical flow from RGB frames, we present a new dual-modal pipeline adopting both RGB frames and the corresponding spike stream as inputs (SVFI). It extracts the scene structure and objects' outline feature maps of the target frames from spike stream. Those feature maps are fused with the color and texture feature maps extracted from RGB frames to synthesize target frames. Benefited by the spike stream that contains consecutive information between two frames, SVFI can directly extract the information in occlusion and motion blur areas of target frames from spike stream, thus it is more robust than previous optical flow-based methods. Experiments show SVFI outperforms the SOTA methods on wide variety of datasets. For instance, in 7 and 15 frame skip evaluations, it shows up to 5.58 dB and 6.56 dB improvements in terms of PSNR over the corresponding second best methods BMBC and DAIN. SVFI also shows visually impressive performance in real-world scenes.