Partial domain adaptation which assumes that the unknown target label space is a subset of the source label space has attracted much attention in computer vision. Despite recent progress, existing methods often suffer from three key problems: negative transfer, lack of discriminability, and domain invariance in the latent space. To alleviate the above issues, we develop a novel 'Select, Label, and Mix' (SLM) framework that aims to learn discriminative invariant feature representations for partial domain adaptation. First, we present an efficient "select" module that automatically filters out the outlier source samples to avoid negative transfer while aligning distributions across both domains. Second, the "label" module iteratively trains the classifier using both the labeled source domain data and the generated pseudo-labels for the target domain to enhance the discriminability of the latent space. Finally, the "mix" module utilizes domain mixup regularization jointly with the other two modules to explore more intrinsic structures across domains leading to a domain-invariant latent space for partial domain adaptation. Extensive experiments on several benchmark datasets for partial domain adaptation demonstrate the superiority of our proposed framework over state-of-the-art methods.
Context-Aware Token Pruning and Discriminative Selective Attention for Transformer Tracking
Department of Computational Mathematics, University of Moratuwa（计算数学系，摩图瓦大学）；Department of Interdisciplinary Studies, University of Jaffna（跨学科研究系，贾夫纳大学）；Department of Computer Science, University of Jaffna（计算机科学系，贾夫纳大学）
Simon Fraser University（西蒙弗雷泽大学）；Siemens Healthineers（西门子医疗）
Skip connections in deep networks have improved both segmentation and classification performance by facilitating the training of deeper network architectures, and reducing the risks for vanishing gradients. They equip encoder-decoder-like networks with richer feature representations, but at the cost of higher memory usage, computation, and possibly resulting in transferring non-discriminative feature maps. In this paper, we focus on improving skip connections used in segmentation networks (e.g., U-Net, V-Net, and The One Hundred Layers Tiramisu (DensNet) architectures). We propose light, learn…
South China University of Technology（华南理工大学）；University of Texas at Arlington（得克萨斯大学阿灵顿分校）；Tencent AI Lab（腾讯人工智能实验室）；The University of Adelaide（阿德莱德大学）
Channel pruning is one of the predominant approaches for deep model compression. Existing pruning methods either train from scratch with sparsity constraints on channels, or minimize the reconstruction error between the pre-trained feature maps and the compressed ones. Both strategies suffer from some limitations: the former kind is computationally expensive and difficult to converge, whilst the latter kind optimizes the reconstruction error but ignores the discriminative power of channels. To overcome these drawbacks, we investigate a simple-yet-effective method, called discrimination-aware c…
Toward Realistic Remote Sensing Dataset Distillation with Discriminative Prototype-guided Diffusion
Computer Vision and Learning Systems, Department of Electrical Engineering, Linköping University（计算机视觉与学习系统，电气工程系，利厄普大学）；Helmholtz-Zentrum Dresden-Rossendorf, Responsible AI Group（海姆霍尔茨·德累斯顿-罗斯托克研究中心，负责任人工智能小组）；University of Iceland, Faculty of Electrical and Computer Engineering（冰岛大学，电气与计算机工程学院）
Correlative and Discriminative Label Grouping for Multi-Label Visual Prompt Tuning
Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University（安徽省多模态认知计算重点实验室，计算机科学与技术学院，安徽大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Nanjing University of Science and Technology（南京理工大学）
School of Computer Science, Wuhan University（武汉大学计算机学院）
Recently contrastive learning has shown significant progress in learning visual representations from unlabeled data. The core idea is training the backbone to be invariant to different augmentations of an instance. While most methods only maximize the feature similarity between two augmented data, we further generate more challenging training samples and force the model to keep predicting discriminative representation on these hard samples. In this paper, we propose MixSiam, a mixture-based approach upon the traditional siamese network. On the one hand, we input two augmented images of an inst…
Jiangnan University（江南大学）；University of Surrey（萨里大学）
With efficient appearance learning models, Discriminative Correlation Filter (DCF) has been proven to be very successful in recent video object tracking benchmarks and competitions. However, the existing DCF paradigm suffers from two major issues, i.e., spatial boundary effect and temporal filter degradation. To mitigate these challenges, we propose a new DCF-based tracking method. The key innovations of the proposed method include adaptive spatial feature selection and temporal consistent constraints, with which the new tracker enables joint spatial-temporal filter learning in a lower dimensi…
Jiangnan University（江南大学）；University of Surrey（萨里大学）
We propose a new Group Feature Selection method for Discriminative Correlation Filters (GFS-DCF) based visual object tracking. The key innovation of the proposed method is to perform group feature selection across both channel and spatial dimensions, thus to pinpoint the structural relevance of multi-channel features to the filtering system. In contrast to the widely used spatial regularisation or feature selection methods, to the best of our knowledge, this is the first time that channel selection has been advocated for DCF-based tracking. We demonstrate that our GFS-DCF method is able to sig…
Tsinghua University（清华大学）；Queen Mary University of London（伦敦玛丽女王大学）
Current person re-identification (ReID) methods typically rely on single-frame imagery features, whilst ignoring space-time information from image sequences often available in the practical surveillance scenarios. Single-frame (single-shot) based visual appearance matching is inherently limited for person ReID in public spaces due to the challenging visual ambiguity and uncertainty arising from non-overlapping camera views where viewing condition changes can cause significant people appearance variations. In this work, we present a novel model to automatically select the most discriminative vi…
Zhejiang University（浙江大学）；University of Technology Sydney（悉尼科技大学）；Baidu Research（百度研究院）
Prevalent semantic segmentation solutions are, in essence, a dense discriminative classifier of p(class|pixel feature). Though straightforward, this de facto paradigm neglects the underlying data distribution p(pixel feature|class), and struggles to identify out-of-distribution data. Going beyond this, we propose GMMSeg, a new family of segmentation models that rely on a dense generative classifier for the joint distribution p(pixel feature,class). For each class, GMMSeg builds Gaussian Mixture Models (GMMs) via Expectation-Maximization (EM), so as to capture class-conditional densities. Meanw…
Institute of Mathematics of the Romanian Academy（罗马尼亚科学院数学研究所）；Faculty of Automatic Control and Computer Science, University Politehnica of Bucharest（布加勒斯特理工大学自动控制与计算机科学学院）；Google Research（谷歌研究院）
Feature selection is an essential problem in computer vision, important for category learning and recognition. Along with the rapid development of a wide variety of visual features and classifiers, there is a growing need for efficient feature selection and combination methods, to construct powerful classifiers for more complex and higher-level recognition tasks. We propose an algorithm that efficiently discovers sparse, compact representations of input features or classifiers, from a vast sea of candidates, with important optimality properties, low computational cost and excellent accuracy in…
Nanjing University of Information Science and Technology（南京信息工程大学）
Recently, the compressive tracking (CT) method has attracted much attention due to its high efficiency, but it cannot well deal with the large scale target appearance variations due to its data-independent random projection matrix that results in less discriminative features. To address this issue, in this paper we propose an adaptive CT approach, which selects the most discriminative features to design an effective appearance model. Our method significantly improves CT in three aspects: Firstly, the most discriminative features are selected via an online vector boosting method. Secondly, the …
School of Automation Science and Electrical engineering, Beihang University（北京航空航天大学自动化科学与电气工程学院）
Features play an important role in various visual tasks, especially in visual place recognition applied in perceptual changing environments. In this paper, we address the challenges of place recognition due to dynamics and confusable patterns by proposing a discriminative and semantic feature selection network, dubbed as DSFeat. Supervised by both semantic information and attention mechanism, we can estimate pixel-wise stability of features, indicating the probability of a static and stable region from which features are extracted, and then select features that are insensitive to dynamic inter…
Nanyang Technological University（南洋理工大学）；Advanced Digital Sciences Center（先进数字科学中心）；City University of Hong Kong（香港城市大学）
In order to encode the class correlation and class specific information in image representation, we propose a new local feature learning approach named Deep Discriminative and Shareable Feature Learning (DDSFL). DDSFL aims to hierarchically learn feature transformation filter banks to transform raw pixel image patches to features. The learned filter banks are expected to: (1) encode common visual patterns of a flexible number of categories; (2) encode discriminative information; and (3) hierarchically extract patterns at different visual levels. Particularly, in each single layer of DDSFL, sha…
University of Maryland（马里兰大学）；Hewlett-Packard Laboratories（惠普实验室）；Nanjing University（南京大学）；University of California, Santa Cruz（加州大学圣克鲁兹分校）
One of the crucial problems in visual tracking is how the object is represented. Conventional appearance-based trackers are using increasingly more complex features in order to be robust. However, complex representations typically not only require more computation for feature extraction, but also make the state inference complicated. We show that with a careful feature selection scheme, extremely simple yet discriminative features can be used for robust object tracking. The central component of the proposed method is a succinct and discriminative representation of the object using discriminati…
Frequency Guidance Matters: Skeletal Action Recognition by Frequency-Aware Mixed Transformer
University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）；Carnegie Mellon University（卡内基梅隆大学）；Microsoft Corporation（微软公司）；University of Central Florida（中佛罗里达大学）
Nanyang Technological University（南洋理工大学）；Indian Institute of Technology, Bombay（印度理工学院孟买分校）；Edinburgh Napier University（爱丁堡龙比亚大学）；Singapore University of Technology and Design（新加坡科技设计大学）
In this paper, we address the problem of action recognition from still images and videos. Traditional local features such as SIFT, STIP etc. invariably pose two potential problems: 1) they are not evenly distributed in different entities of a given category and 2) many of such features are not exclusive of the visual concept the entities represent. In order to generate a dictionary taking the aforementioned issues into account, we propose a novel discriminative method for identifying robust and category specific local features which maximize the class separability to a greater extent. Specific…
Synergizing Discriminative Exemplars and Self-Refined Experience for MLLM-based In-Context Learning in Medical Diagnosis
CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所分子影像重点实验室）；School of Software, North University of China（中北大学软件学院）
Key Laboratory of Ministry of Education for Image Processing and Intelligent ControlSchool of Automation, Huazhong University of Science and Technology（华中科技大学自动化学院图像处理与智能控制教育部重点实验室）；Key Laboratory of Machine Perception, Peking University（北京大学机器感知重点实验室）；Megvii Inc. (Face++)（旷视科技（Face++））
Most existing methods of semantic segmentation still suffer from two aspects of challenges: intra-class inconsistency and inter-class indistinction. To tackle these two problems, we propose a Discriminative Feature Network (DFN), which contains two sub-networks: Smooth Network and Border Network. Specifically, to handle the intra-class inconsistency problem, we specially design a Smooth Network with Channel Attention Block and global average pooling to select the more discriminative features. Furthermore, we propose a Border Network to make the bilateral features of boundary distinguishable wi…
University of Adelaide（阿德莱德大学）；Australian Centre for Robotic Vision（澳大利亚机器人视觉中心）
The introduction of low-cost RGB-D sensors has promoted the research in skeleton-based human action recognition. Devising a representation suitable for characterising actions on the basis of noisy skeleton sequences remains a challenge, however. We here provide two insights into this challenge. First, we show that the discriminative information of a skeleton sequence usually resides in a short temporal interval and we propose a simple-but-effective local descriptor called trajectorylet to capture the static and kinematic information within this interval. Second, we further propose to encode ea…
Singapore University of Technology and Design（新加坡科技设计大学）；The University of Adelaide（阿德莱德大学）
Convolutional Neural Network (CNN) is a very powerful approach to extract discriminative local descriptors for effective image search. Recent work adopts fine-tuned strategies to further improve the discriminative power of the descriptors. Taking a different approach, in this paper, we propose a novel framework to achieve competitive retrieval performance. Firstly, we propose various masking schemes, namely SIFT-mask, SUM-mask, and MAX-mask, to select a representative subset of local convolutional features and remove a large number of redundant features. We demonstrate that this can effectivel…
The gap in representations between image and video makes Image-to-Video Re-identification (I2V Re-ID) challenging, and recent works formulate this problem as a knowledge distillation (KD) process. In this paper, we propose a mutual discriminative knowledge distillation framework to transfer a video-based richer representation to an image based representation more effectively. Specifically, we propose the triplet contrast loss (TCL), a novel loss designed for KD. During the KD process, the TCL loss transfers the local structure, exploits the higher order information, and mitigates the misalignm…
University of Warwick（华威大学）；Griffith University（格里菲斯大学）
The core for tackling the fine-grained visual categorization (FGVC) is to learn subtle yet discriminative features. Most previous works achieve this by explicitly selecting the discriminative parts or integrating the attention mechanism via CNN-based approaches.However, these methods enhance the computational complexity and make the modeldominated by the regions containing the most of the objects. Recently, vision trans-former (ViT) has achieved SOTA performance on general image recognition tasks. Theself-attention mechanism aggregates and weights the information from all patches to the classi…
DFST proposes an optimized visual tracking algorithm based on the real-time selection of locally and temporally discriminative features. A feature selection mechanism is embedded in the Adaptive colour Names (CN) tracking system that adaptively selects the top-ranked discriminative features for tracking. DFST provides a significant gain in accuracy and precision allowing the use of a dynamic set of features that results in an increased system flexibility. DFST is based on the unsupervised method "Infinite Feature Selection" (Inf-FS), which ranks features according with their "redundancy" witho…
Indian Statistical Institute（印度统计研究所）；Jadavpur University（贾达普大学）
In this paper a nonlinear Gabor Wavelet Transform (GWT) discriminant feature extraction approach for enhanced face recognition is proposed. Firstly, the low-energized blocks from Gabor wavelet transformed images are extracted. Secondly, the nonlinear discriminating features are analyzed and extracted from the selected low-energized blocks by the generalized Kernel Discriminative Common Vector (KDCV) method. The KDCV method is extended to include cosine kernel function in the discriminating method. The KDCV with the cosine kernels is then applied on the extracted low energized discriminating fe…
In recent years, deep discriminative models have achieved extraordinary performance on supervised learning tasks, significantly outperforming their generative counterparts. However, their success relies on the presence of a large amount of labeled data. How can one use the same discriminative models for learning useful features in the absence of labels? We address this question in this paper, by jointly modeling the distribution of data and latent features in a manner that explicitly assigns zero probability to unobserved data. Rather than maximizing the marginal probability of observed data, …
Microsoft Mixed Reality and AI Zurich lab（微软混合现实与人工智能苏黎世实验室）；Department of Computer Science, ETH Zurich（苏黎世联邦理工学院计算机科学系）
To be invariant, or not to be invariant: that is the question formulated in this work about local descriptors. A limitation of current feature descriptors is the trade-off between generalization and discriminative power: more invariance means less informative descriptors. We propose to overcome this limitation with a disentanglement of invariance in local descriptors and with an online selection of the most appropriate invariance given the context. Our framework consists in a joint learning of multiple local descriptors with different levels of invariance and of meta descriptors encoding the r…
With various face presentation attacks emerging continually, face anti-spoofing (FAS) approaches based on domain generalization (DG) have drawn growing attention. Existing DG-based FAS approaches always capture the domain-invariant features for generalizing on the various unseen domains. However, they neglect individual source domains' discriminative characteristics and diverse domain-specific information of the unseen domains, and the trained model is not sufficient to be adapted to various unseen domains. To address this issue, we propose an Adaptive Mixture of Experts Learning (AMEL) framew…
Hefei University of Technology（合肥工业大学）；National University of Singapore（新加坡国立大学）
Sparse Representation (or coding) based Classification (SRC) has gained great success in face recognition in recent years. However, SRC emphasizes the sparsity too much and overlooks the correlation information which has been demonstrated to be critical in real-world face recognition problems. Besides, some work considers the correlation but overlooks the discriminative ability of sparsity. Different from these existing techniques, in this paper, we propose a framework called Adaptive Sparse Representation based Classification (ASRC) in which sparsity and correlation are jointly considered. Sp…
University of British Columbia（不列颠哥伦比亚大学）；Vector Institute for AI（人工智能向量研究所）；University of Fukui（福井大学）；Massachusetts Institute of Technology（麻省理工学院）；University of Strasbourg（斯特拉斯堡大学）
A-SelecT: Automatic Timestep Selection for Diffusion Transformer Representation Learning
University of Missouri–Kansas City（密苏里大学堪萨斯城分校）；U. S. Naval Research Laboratory（美国海军研究实验室）；Lamar University（拉马尔大学）；University of Florida（佛罗里达大学）；University of Texas Rio Grande Valley（德克萨斯大学里奥格兰德河谷分校）；University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；Meta AI；Rochester Institute of Technology（罗切斯特理工学院）
In this paper, we propose a simple but effective semantic-based aggregation (SBA) method. The proposed SBA utilizes the discriminative filters of deep convolutional layers as semantic detectors. Moreover, we propose the effective unsupervised strategy to select some semantic detectors to generate the "probabilistic proposals", which highlight certain discriminative pattern of objects and suppress the noise of background. The final global SBA representation could then be acquired by aggregating the regional representations weighted by the selected "probabilistic proposals" corresponding to vari…
Huazhong Univerisity of Science and Technology（华中科技大学）；Tsinghua University（清华大学）
Discriminative representation is crucial for the association step in multi-object tracking. Recent work mainly utilizes features in single or neighboring frames for constructing metric loss and empowering networks to extract representation of targets. Although this strategy is effective, it fails to fully exploit the information contained in a whole trajectory. To this end, we propose a strategy, namely multi-view trajectory contrastive learning, in which each trajectory is represented as a center vector. By maintaining all the vectors in a dynamically updated memory bank, a trajectory-level c…
Recently, there are increasing interests in inferring mirco-expression from facial image sequences. Due to subtle facial movement of micro-expressions, feature extraction has become an important and critical issue for spontaneous facial micro-expression recognition. Recent works usually used spatiotemporal local binary pattern for micro-expression analysis. However, the commonly used spatiotemporal local binary pattern considers dynamic texture information to represent face images while misses the shape attribute of face images. On the other hand, their works extracted the spatiotemporal featu…
Korea Institute of Science and Technology Europe Forschungsgesellschaft mbH（韩国科学技术院欧洲研究所）；TU Kaiserslautern（凯泽斯劳滕工业大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）
The data imbalance problem is a frequent bottleneck in the classification performance of neural networks. In this paper, we propose a novel supervised discriminative feature generation (DFG) method for a minority class dataset. DFG is based on the modified structure of a generative adversarial network consisting of four independent networks: generator, discriminator, feature extractor, and classifier. To augment the selected discriminative features of the minority class data by adopting an attention mechanism, the generator for the class-imbalanced target task is trained, and the feature extra…
Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features
Institute of Information Engineering, CAS（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；Institute of Computing Technology, CAS（中国科学院计算技术研究所）；Peng Cheng Laboratory（鹏城实验室）；School of Cyber Science and Tech., Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区网络空间科学与技术学院）；School of Computer Science and Tech., University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术学院）
The margin-based softmax loss functions greatly enhance intra-class compactness and perform well on the tasks of face recognition and object classification. Outperformance, however, depends on the careful hyperparameter selection. Moreover, the hard angle restriction also increases the risk of overfitting. In this paper, angular loss suggested by maximizing the angular gradient to promote intra-class compactness avoids overfitting. Besides, our method has only one adjustable constant for intra-class compactness control. We define three metrics to measure inter-class separability and intra-clas…
We propose a Bayesian approach to learn discriminative dictionaries for sparse representation of data. The proposed approach infers probability distributions over the atoms of a discriminative dictionary using a Beta Process. It also computes sets of Bernoulli distributions that associate class labels to the learned dictionary atoms. This association signifies the selection probabilities of the dictionary atoms in the expansion of class-specific data. Furthermore, the non-parametric character of the proposed approach allows it to infer the correct size of the dictionary. We exploit the aforeme…
University of California, Berkeley（加州大学伯克利分校）；Tsinghua University（清华大学）
To learn intrinsic low-dimensional structures from high-dimensional data that most discriminate between classes, we propose the principle of Maximal Coding Rate Reduction ($\text{MCR}^2$), an information-theoretic measure that maximizes the coding rate difference between the whole dataset and the sum of each individual class. We clarify its relationships with most existing frameworks such as cross-entropy, information bottleneck, information gain, contractive and contrastive learning, and provide theoretical guarantees for learning diverse and discriminative features. The coding rate can be ac…
Sun Yat-sen University（中山大学）；Huizhou University（惠州学院）
This work investigates how the traditional image classification pipelines can be extended into a deep architecture, inspired by recent successes of deep neural networks. We propose a deep boosting framework based on layer-by-layer joint feature boosting and dictionary learning. In each layer, we construct a dictionary of filters by combining the filters from the lower layer, and iteratively optimize the image representation with a joint discriminative-generative formulation, i.e. minimization of empirical classification error plus regularization of analysis image generation over training image…
Embedded Multi-label Feature Selection via Orthogonal Regression
Faculty of Information Technology, Beijing University of Technology（北京工业大学信息学部）；School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）；Guangdong Artificial Intelligence and Digital Economy Laboratory（广东省人工智能与数字经济实验室）
Guangdong University of Technology（广东工业大学）；Shenzhen University（深圳大学）；The Hong Kong Polytechnic University（香港理工大学）；The University of Queensland（昆士兰大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））
This paper proposes a novel discriminative regression method, called adaptive locality preserving regression (ALPR) for classification. In particular, ALPR aims to learn a more flexible and discriminative projection that not only preserves the intrinsic structure of data, but also possesses the properties of feature selection and interpretability. To this end, we introduce a target learning technique to adaptively learn a more discriminative and flexible target matrix rather than the pre-defined strict zero-one label matrix for regression. Then a locality preserving constraint regularized by t…
In the last decade, traditional dictionary learning methods have been successfully applied to various pattern classification tasks. Although these methods produce sparse representations of signals which are robust against distortions and missing data, such representations quite often turn out to be unsuitable if the final objective is signal classification. In order to overcome or at least to attenuate such a weakness, several new methods which incorporate discriminative information into sparse-inducing models have emerged in recent years. In particular, methods for discriminative dictionary l…
University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Worcester Polytechnic Institute（伍斯特理工学院）；Tsinghua University（清华大学）；Northwestern University（西北大学）
Mining discriminative subgraph patterns from graph data has attracted great interest in recent years. It has a wide variety of applications in disease diagnosis, neuroimaging, etc. Most research on subgraph mining focuses on the graph representation alone. However, in many real-world applications, the side information is available along with the graph data. For example, for neurological disorder identification, in addition to the brain networks derived from neuroimaging data, hundreds of clinical, immunologic, serologic and cognitive measures may also be documented for each subject. These meas…
