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International Conference on Automatic Face and Gesture Recognition

作者： Qixiang Geng Dong Liang Huiyu Zhou Liyan Zhang Han Sun Ningzhong Liu MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Collaborative Innovation Center of Novel Software Technology and Industrialization College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics School of Informatics University of Leicester

ISBN: (纸本)9781665431774

The appearance degradation caused by low resolution is the core problem of small face detection. Therefore, a natural approach is to assemble information from the context. This paper focuses on how to use high-level contextual information to improve the abilities of anchor-based detectors to detect dense and degenerate faces. We tap the spatial contextual information on the overall view based on the density map, and propose the prior of face co-occurrence for inferred bounding-boxes coordination. We also propose score-size-specific non-maximum suppression to replace the traditional non-maximum suppression at the end of anchor-based detectors. According to the inferred face boxes' quantity, score and size, the proposed synthetical solution reduces false positives and increases true positives. Our method does not require additional training, which is model-independent and can be embedded into existing face detectors. We also propose a dataset - Crowd Face for face detection, which is full of challenges. We expect to supply enough samples to highlight the difficulties of detecting dense and degenerate faces. We embed our proposed methods into state-of-the-art face detectors on massively benchmarked face datasets. Compared with the prior art on the WIDER FACE hard set, our method increase an Average Precision of 0.1 %-1.3%. On Crowd Face, it increases an Average Precision of 1 % – 6%. Dataset is available on: https://***/QxGeng/Crowd-Face.

关键词： Training Degradation Face recognition Conferences Detectors Gesture recognition Benchmark testing

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XL2Bench: A Benchmark for Extremely Long Context Understanding with Long-range Dependencies

arXiv

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arXiv 2024年

作者： Ni, Xuanfan Cai, Hengyi Wei, Xiaochi Wang, Shuaiqiang Yin, Dawei Li, Piji Nanjing University of Aeronautics and Astronautics Nanjing China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China Institute of Computing Technology CAS Beijing China Baidu Inc. Beijing China

Large Language Models (LLMs) have demonstrated remarkable performance across diverse tasks but are constrained by their small context window sizes. Various efforts have been proposed to expand the context window to accommodate even up to 200K input tokens. Meanwhile, building high-quality benchmarks with much longer text lengths and more demanding tasks to provide comprehensive evaluations is of immense practical interest to facilitate long context understanding research of LLMs. However, prior benchmarks create datasets that ostensibly cater to long-text comprehension by expanding the input of traditional tasks, which falls short to exhibit the unique characteristics of long-text understanding, including long dependency tasks and longer text length compatible with modern LLMs’ context window size. In this paper, we introduce a benchmark for eXtremely Long context understanding with Long-range dependencies, XL2Bench, which includes three scenarios—Fiction Reading, Paper Reading, and Law Reading—and four tasks of increasing complexity: Memory Retrieval, Detailed Understanding, Overall Understanding, and Open-ended Generation, covering 27 subtasks in English and Chinese. It has an average length of 100K+ words (English) and 200K+ characters (Chinese). Evaluating six leading LLMs on XL2Bench, we find that their performance significantly lags behind human levels. Moreover, the observed decline in performance across both the original and enhanced datasets underscores the efficacy of our approach to mitigating data contamination. © 2024, CC BY.

关键词： Benchmarking

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Better safe than sorry: Preventing delusive adversaries with adversarial training

arXiv

引用

arXiv 2021年

作者： Tao, Lue Feng, Lei Yi, Jinfeng Huang, Sheng-Jun Chen, Songcan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence China College of Computer Science Chongqing University China JD AI Research China

Delusive attacks aim to substantially deteriorate the test accuracy of the learning model by slightly perturbing the features of correctly labeled training examples. By formalizing this malicious attack as finding the worst-case training data within a specific ∞-Wasserstein ball, we show that minimizing adversarial risk on the perturbed data is equivalent to optimizing an upper bound of natural risk on the original data. This implies that adversarial training can serve as a principled defense against delusive attacks. Thus, the test accuracy decreased by delusive attacks can be largely recovered by adversarial training. To further understand the internal mechanism of the defense, we disclose that adversarial training can resist the delusive perturbations by preventing the learner from overly relying on non-robust features in a natural setting. Finally, we complement our theoretical findings with a set of experiments on popular benchmark datasets, which show that the defense withstands six different practical attacks. Both theoretical and empirical results vote for adversarial training when confronted with delusive adversaries. Copyright © 2021, The Authors. All rights reserved.

关键词： Network security

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AdaGDA: Faster Adaptive Gradient Descent Ascent Methods for Minimax Optimization

arXiv

引用

arXiv 2021年

作者： Huang, Feihu Wu, Xidong Hu, Zhengmian College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Miit Key Laboratory of Pattern Analysis and Machine Intelligence China Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh United States

In the paper, we propose a class of faster adaptive Gradient Descent Ascent (GDA) methods for solving the nonconvex-strongly-concave minimax problems by using the unified adaptive matrices, which include almost all existing coordinate-wise and global adaptive learning rates. In particular, we provide an effective convergence analysis framework for our adaptive GDA methods. Specifically, we propose a fast Adaptive Gradient Descent Ascent (AdaGDA) method based on the basic momentum technique, which reaches a lower gradient complexity of Õ(κ4ϵ-4) for finding an ϵ-stationary point without large batches, which improves the existing results of the adaptive GDA methods by a factor of O(√κ). Moreover, we propose an accelerated version of AdaGDA (VR-AdaGDA) method based on the momentum-based variance reduced technique, which achieves a lower gradient complexity of Õ(κ 4.5ϵ-3) for finding an ϵ-stationary point without large batches, which improves the existing results of the adaptive GDA methods by a factor of O(ϵ-1). Moreover, we prove that our VR-AdaGDA method can reach the best known gradient complexity of Õ(κ3ϵ-3) with the minibatch size O(κ3). The experiments on policy evaluation and fair classifier learning tasks are conducted to verify the efficiency of our new algorithms. © 2021, CC BY-NC-SA.

关键词： Gradient methods

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Learning twofold heterogeneous multi-task by sharing similar convolution kernel pairs

arXiv

引用

arXiv 2021年

作者： Feng, Quan Chen, Songcan College of Computer Science & Technology Nanjing University of Aeronautics & Astronautics NanjingJiangsu211106 China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics & Astronautics NanjingJiangsu211106 China

Heterogeneous multi-task learning (HMTL) is an important topic in multi-task learning (MTL). Most existing HMTL methods usually solve either scenario where all tasks reside in the same input (feature) space yet unnecessarily the consistent output (label) space or scenario where their input (feature) spaces are heterogeneous while the output (label) space is consistent. However, to the best of our knowledge, there is limited study on twofold heterogeneous MTL (THMTL) scenario where the input and the output spaces are both inconsistent or heterogeneous. In order to handle this complicated scenario, in this paper, we design a simple and effective multi-task adaptive learning (MTAL) network to learn multiple tasks in such THMTL setting. Specifically, we explore and utilize the inherent relationship between tasks for knowledge sharing from similar convolution kernels in individual layers of the MTAL network. Then in order to realize the sharing, we weightedly aggregate any pair of convolutional kernels with their similarity greater than some threshold ρ, consequently, our model effectively performs cross-task learning while suppresses the intra-redundancy of the entire network. Finally, we conduct end-to-end training. Our experimental results demonstrate the effectiveness of our method in comparison with the state-of-the-art counterparts. Copyright © 2021, The Authors. All rights reserved.

关键词： Linearization

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Learning Multi-Tasks with Inconsistent Labels by using Auxiliary Big Task

arXiv

引用

arXiv 2022年

作者： Feng, Quan Chen, Songcan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing211106 China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing211106 China

Multi-task learning is to improve the performance of the model by transferring and exploiting common knowledge among tasks. Existing MTL works mainly focus on the scenario where label sets among multiple tasks (MTs) are usually the same, thus they can be utilized for learning across the tasks. While almost rare works explore the scenario where each task only has a small amount of training samples, and their label sets are just partially overlapped or even not. Learning such MTs is more challenging because of less correlation information available among these tasks. For this, we propose a framework to learn these tasks by jointly leveraging both abundant information from a learnt auxiliary big task with sufficiently many classes to cover those of all these tasks and the information shared among those partially-overlapped tasks. In our implementation of using the same neural network architecture of the learnt auxiliary task to learn individual tasks, the key idea is to utilize available label information to adaptively prune the hidden layer neurons of the auxiliary network to construct corresponding network for each task, while accompanying a joint learning across individual tasks. Our experimental results demonstrate its effectiveness in comparison with the state-of-the-art approaches. Copyright © 2022, The Authors. All rights reserved.

关键词： Network architecture

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Unsupervised domain adaptation with progressive adaptation of subspaces

arXiv

引用

arXiv 2020年

作者： Li, Weikai Chen, Songcan College of Computer Science & Technology Nanjing University of Aeronautics & Astronautics Nanjing Jiangsu211106 China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics & Astronautics Nanjing Jiangsu211106 China

Unsupervised Domain Adaptation (UDA) aims to classify unlabeled target domain by transferring knowledge from labeled source domain with domain shift. Most of the existing UDA methods try to mitigate the adverse impact induced by the shift via reducing domain discrepancy. However, such approaches easily suffer a notorious mode collapse issue due to the lack of labels in target domain. Naturally, one of the effective ways to mitigate this issue is to reliably estimate the pseudo labels for target domain, which itself is hard. To overcome this, we propose a novel UDA method named Progressive Adaptation of Subspaces approach (PAS) in which we utilize such an intuition that appears much reasonable to gradually obtain reliable pseudo labels. Specifically, we progressively and steadily refine the shared subspaces as bridge of knowledge transfer by adaptively anchoring/selecting and leveraging those target samples with reliable pseudo labels. Subsequently, the refined subspaces can in turn provide more reliable pseudo-labels of the target domain, making the mode collapse highly mitigated. Our thorough evaluation demonstrates that PAS is not only effective for common UDA, but also outperforms the state-of-the arts for more challenging Partial Domain Adaptation (PDA) situation, where the source label set subsumes the target *** Codes 68T10 Copyright © 2020, The Authors. All rights reserved.

关键词： Knowledge management

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Topology Reorganized Graph Contrastive Learning with Mitigating Semantic Drift

arXiv

引用

arXiv 2024年

作者： Zhang, Jiaqiang Chen, Songcan College of Computer Science & Technology Nanjing University of Aeronautics & Astronautics Jiangsu Nanjing211106 China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics & Astronautics Jiangsu Nanjing211106 China

Graph contrastive learning (GCL) is an effective paradigm for node representation learning in graphs. The key components hidden behind GCL are data augmentation and positive-negative pair selection. Typical data augmentations in GCL, such as uniform deletion of edges, are generally blind and resort to local perturbation, which is prone to producing under-diversity views. Additionally, there is a risk of making the augmented data traverse to other classes. Moreover, most methods always treat all other samples as negatives. Such a negative pairing naturally results in sampling bias and likewise may make the learned representation suffer from semantic drift. Therefore, to increase the diversity of the contrastive view, we propose two simple and effective global topological augmentations to compensate current GCL. One is to mine the semantic correlation between nodes in the feature space. The other is to utilize the algebraic properties of the adjacency matrix to characterize the topology by eigen-decomposition. With the help of both, we can retain important edges to build a better view. To reduce the risk of semantic drift, a prototype-based negative pair selection is further designed which can filter false negative samples. Extensive experiments on various tasks demonstrate the advantages of the model compared to the state-of-the-art methods. © 2024, CC BY.

关键词： Graph neural networks

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Pathformer3D: A 3D Scanpath Transformer for 360° Images

arXiv

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arXiv 2024年

作者： Quan, Rong Lai, Yantao Qiu, Mengyu Liang, Dong College of Artificial Intelligence Nanjing University of Aeronautics and Astronautics The Key Laboratory of Brain-Machine Intelligence Technology Ministry of Education Nanjing211106 China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Shenzhen Research Institute Nanjing University of Aeronautics and Astronautics Shenzhen China

Scanpath prediction in 360° images can help realize rapid rendering and better user interaction in Virtual/Augmented Reality applications. However, existing scanpath prediction models for 360° images execute scanpath prediction on 2D equirectangular projection plane, which always result in big computation error owing to the 2D plane’s distortion and coordinate discontinuity. In this work, we perform scanpath prediction for 360° images in 3D spherical coordinate system and proposed a novel 3D scanpath Transformer named Pathformer3D. Specifically, a 3D Transformer encoder is first used to extract 3D contextual feature representation for the 360° image. Then, the contextual feature representation and historical fixation information are input into a Transformer decoder to output current time step’s fixation embedding, where the self-attention module is used to imitate the visual working memory mechanism of human visual system and directly model the time dependencies among the fixations. Finally, a 3D Gaussian distribution is learned from each fixation embedding, from which the fixation position can be sampled. Evaluation on four panoramic eye-tracking datasets demonstrates that Pathformer3D outperforms the current state-of-the-art methods. Code is available at https://***/lsztzp/ Pathformer3D. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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FedDA: Faster Framework of Local Adaptive Gradient Methods via Restarted Dual Averaging

arXiv

引用

arXiv 2023年

作者： Li, Junyi Huang, Feihu Huang, Heng Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh United States College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China

Federated learning (FL) is an emerging learning paradigm to tackle massively distributed data. In Federated Learning, a set of clients jointly perform a machine learning task under the coordination of a server. The FedAvg algorithm is one of the most widely used methods to solve Federated Learning problems. In FedAvg, the learning rate is a constant rather than changing adaptively. The adaptive gradient methods show superior performance over the constant learning rate schedule;however, there is still no general framework to incorporate adaptive gradient methods into the federated setting. In this paper, we propose FedDA, a novel framework for local adaptive gradient methods. The framework adopts a restarted dual averaging technique and is flexible with various gradient estimation methods and adaptive learning rate formulations. In particular, we analyze FedDA-MVR, an instantiation of our framework, and show that it achieves gradient complexity Õ(∊−1.5) and communication complexity Õ(∊−1) for finding a stationary point ∊. This matches the best known rate for first-order FL algorithms and FedDA-MVR is the first adaptive FL algorithm that achieves this rate. We also perform extensive numerical experiments to verify the efficacy of our method. Copyright © 2023, The Authors. All rights reserved.

关键词： Learning algorithms

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