Continual Generalized Category Discovery (C-GCD) requires identifying novel classes from unlabeled data while retaining knowledge of known classes over time. Existing methods typically update classifier weights dynamically, resulting in forgetting and inconsistent feature alignment. We propose GOAL, a unified framework that introduces a fixed Equiangular Tight Frame (ETF) classifier to impose a consistent geometric structure throughout learning. GOAL conducts supervised alignment for labeled samples and confidence-guided alignment for novel samples, enabling stable integration of new classes without disrupting old ones. Experiments on four benchmarks show that GOAL outperforms prior methods, reducing forgetting by 16.1% and boosting novel class discovery by 3.2%, establishing a strong solution for long-horizon continual discovery.

This paper focuses on the Continual Test-Time Adaptation (CTTA) task, aiming to enable an agent to continuously adapt to evolving target domains while retaining previously acquired domain knowledge for effective reuse when those domains reappear. Existing shared-parameter paradigms struggle to balance adaptation and forgetting, leading to decreased efficiency and stability. To address this, we propose a frequency-aware shared and self-adaptive expert framework, consisting of two key components: (i) a dual-branch expert architecture that extracts general features and dynamically models domain-specific representations, effectively reducing cross-domain interference and repetitive learning cost; and (ii) an online Frequency-aware Domain Discriminator (FDD), which leverages the robustness of low-frequency image signals for online domain shift detection, guiding dynamic allocation of expert resources for more stable and realistic adaptation. Additionally, we introduce a Continual Repeated Shifts (CRS) benchmark to simulate periodic domain changes for more realistic evaluation. Experimental results show that our method consistently outperforms existing approaches on both classification and segmentation CTTA tasks under standard and CRS settings, with ablations and visualizations confirming its effectiveness and robustness.

Visual-language models (VLMs) like CLIP exhibit strong generalization but struggle with distribution shifts at test time. Existing training-free test-time adaptation (TTA) methods operate strictly within CLIP’s original feature space, relying on high-confidence samples while overlooking the potential of low-confidence ones. We propose MS-TTA, a training-free approach that enhances feature representations beyond CLIP’s space using a single-step k-nearest neighbors (kNN) Mean-Shift. By refining all test samples, MS-TTA improves feature compactness and class separability, leading to more stable adaptation. Additionally, a cache of refined embeddings further enhances inference by providing Mean-Shift-enhanced logits. Extensive evaluations on OOD and Cross-Dataset Benchmarks demonstrate that MS-TTA consistently outperforms state-of-the-art training-free TTA methods, achieving robust adaptation without requiring additional training.

Few-shot class-incremental Learning (FSCIL) enables models to learn new classes from limited data while retaining performance on previously learned classes. Traditional FSCIL methods often require fine-tuning parameters with limited new class data and suffer from a separation between learning new classes and utilizing old knowledge. Inspired by the analogical learning mechanisms of the human brain, we propose a novel analogical generative method. Our approach includes the Brain-Inspired Analogical Generator (BiAG), which derives new class weights from existing classes without parameter fine-tuning during incremental stages. BiAG consists of three components: Weight Self-Attention Module (WSA), Weight & Prototype Analogical Attention Module (WPAA), and Semantic Conversion Module (SCM). SCM uses Neural Collapse theory for semantic conversion, WSA supplements new class weights, and WPAA computes analogies to generate new class weights. Experiments on miniImageNet, CUB-200, and CIFAR-100 datasets demonstrate that our method achieves higher final and average accuracy compared to SOTA methods.

This paper focuses on domain incremental learning (DIL) for multiple vision tasks, including object detection, instance segmentation, and image classification. DIL aims to adapt a model to new domains over time without forgetting previously acquired knowledge. Recent DIL methods append learnable prompts to input embeddings of a frozen base model to learn from new domains. However, due to prompts' limited representation ability, they struggle to adapt the feature space to new domain data distributions. To overcome this limitation, we propose a novel DIL method named Domain Difference Adapters (DD-Adapters). Through feature visualization and singular value analysis, we identify the cross-domain clustering ability of the base model and the low-rank property of domain difference. Based on these insights, our method imposes low-rank constraints on the base model to capture the principal components of domain differences, while freezing the base model to maintain its cross-domain clustering ability, thereby adapting to new domains effectively. Additionally, we introduce a prototype-guided domain selector (PDS) to dynamically select the appropriate DD-Adapters during inference, mitigating catastrophic forgetting in DIL. Extensive experimental evaluations on eight benchmark datasets demonstrate the performance superiority of the proposed method on three vision tasks, with minimal extra parameter usage.

Domain-Incremental Learning (DIL) enables vision models to adapt to changing conditions in real-world environments while maintaining the knowledge acquired from previous domains. Given privacy concerns and training time, Rehearsal-Free DIL (RFDIL) is more practical. Inspired by the incremental cognitive process of the human brain, we design Dual-level Concept Prototypes (DualCP) for each class to address the conflict between learning new knowledge and retaining old knowledge in RFDIL. To construct DualCP, we propose a Concept Prototype Generator (CPG) that generates both coarse-grained and fine-grained prototypes for each class. Additionally, we introduce a Coarse-to-Fine calibrator (C2F) to align image features with DualCP. Finally, we propose a Dual Dot-Regression (DDR) loss function to optimize our C2F module. Extensive experiments on the DomainNet, CDDB, and CORe50 datasets demonstrate the effectiveness of our method.

In this paper, we focus on a challenging Incremental Object Detection (IOD) problem. Existing IOD methods adopt an image-to-annotation alignment paradigm, which attempts to complete the absent old category annotations and learns both new and old categories concurrently in new tasks. This paradigm inherently introduces missing/redundant/inaccurate annotations of old categories, resulting in a suboptimal performance. Instead, we propose a novel annotation-to-instance alignment IOD paradigm and develop a corresponding method named Learning Endogenous Attention (LEA). Inspired by the human brain, LEA enables the model to focus on annotated task-specific objects, while ignoring irrelevant ones, thus solving the annotation incomplete problem in IOD. Concretely, our LEA consists of Endogenous Attention Modules (EAMs) and an Energy-Based Task Modulator (ETM). During training, we add the dedicated EAMs for each new task and train them to focus on the new categories. During testing, ETM predicts task IDs using energy functions, directing the model to detect task-specific objects. The detection results corresponding to all task IDs are combined as the final output, thereby alleviating the catastrophic forgetting of old knowledge. Extensive experiments on COCO 2017 and Pascal VOC 2007 demonstrate the effectiveness of our method1.

Non-exemplar Class Incremental Learning (NECIL) enables models to continuously acquire new classes without retraining from scratch and storing old task exemplars, addressing privacy and storage issues. However, the absence of data from earlier tasks exacerbates the challenge of catastrophic forgetting in NECIL. In this paper, we propose a novel framework called Dynamic Integration of task-specific Adapters (DIA), which comprises two key components: Task-Specific Adapter Integration (TSAI) and Patch-Level Model Alignment. TSAI boosts compositionality through a patch-level adapter integration strategy, aggregating richer task-specific information while maintaining low computation costs. Patch-Level Model Alignment maintains feature consistency and accurate decision boundaries via two specialized mechanisms: Patch-Level Distillation Loss (PDL) and Patch-Level Feature Reconstruction (PFR). Specifically, on the one hand, the PDL preserves feature-level consistency between successive models by implementing a distillation loss based on the contributions of patch tokens to new class learning. On the other hand, the PFR promotes classifier alignment by reconstructing old class features from previous tasks that adapt to new task knowledge, thereby preserving well-calibrated decision boundaries. Comprehensive experiments validate the effectiveness of our DIA, revealing significant improvements on NECIL benchmark datasets while maintaining an optimal balance between computational complexity and accuracy.

Deep neural networks (DNNs) often underperform in real-world, dynamic settings where data distributions change over time. Domain Incremental Learning (DIL) offers a solution by enabling continuous model adaptation, with Parameter-Isolation DIL (PIDIL) emerging as a promising paradigm to reduce knowledge conflicts. However, existing PIDIL methods struggle with parameter selection accuracy, especially as the number of domains and corresponding classes grows. To address this, we propose SOYO, a lightweight framework that improves domain selection in PIDIL. SOYO introduces a Gaussian Mixture Compressor (GMC) and Domain Feature Resampler (DFR) to store and balance prior domain data efficiently, while a Multi-level Domain Feature Fusion Network (MDFN) enhances domain feature extraction. Our framework supports multiple Parameter-Efficient Fine-Tuning (PEFT) methods and is validated across tasks such as image classification, object detection, and speech enhancement. Experimental results on six benchmarks demonstrate SOYO’s consistent superiority over existing baselines, showcasing its robustness and adaptability in complex, evolving environments.

Few-shot parameter-efficient tuning methods demonstrate promising potential for Vision-Language (V-L) models in downstream tasks. However, existing approaches primarily focus on class-level alignment between image and text features, overlooking crucial instance-specific semantic information. This limitation leads to suboptimal performance on challenging tasks and restricted generalization capability to unseen data. To address these issues, we propose Class- and Instance-aware Adaptation (CIA), a novel framework that simultaneously optimizes both class-level and instance-level alignments. Specifically, CIA introduces a novel instance encoder that leverages cross-modal self-attention to generate instance-specific text features, accompanied by a carefully designed regularization mechanism to maintain consistency between class-level and instance-level representations. Extensive experiments across 15 benchmark datasets demonstrate that CIA significantly improves the downstream adaptation of V-L models.

Generalized Category Discovery (GCD) focuses on classifying known categories while simultaneously discovering novel categories from unlabeled data. However, previous GCD methods face challenges due to inconsistent optimization objectives and category confusion. This leads to feature overlap and ultimately hinders performance on novel categories. To address these issues, we propose the Neural Collapse-inspired Generalized Category Discovery (NC-GCD) framework. By pre-assigning and fixing Equiangular Tight Frame (ETF) prototypes, our method ensures an optimal geometric structure and a consistent optimization objective for both known and novel categories. We introduce a Consistent ETF Alignment Loss that unifies supervised and unsupervised ETF alignment and enhances category separability. Additionally, a Semantic Consistency Matcher (SCM) is designed to maintain stable and consistent label assignments across clustering iterations. Our method significantly enhancing novel category accuracy and demonstrating its effectiveness.

In dynamic real-world scenarios, continuous learning without forgetting old knowledge is essential, particularly in environments with stricter privacy protection or resource-constrained edge devices where storing old exemplars is infeasible. Therefore, Non-Exemplar Class-Incremental Learning (NECIL) has garnered significant attention. Compared with normal settings, it faces a more severe plasticity-stability dilemma and classifier bias. To address those challenges, we propose a framework based on the vision transformer architecture, called the Continual Expansion and Absorption Transformer (CEAT), which consists of two core components. First, we propose the Continual Expansion and Absorption (CEA) method to alleviate the trade-off between new and old classes by parallelly expanding a set of parameters (i.e. EF layer) on the backbone to learn new tasks, while freezing the backbone to retain old task knowledge. The EF layers can be seamlessly absorbed into the ViT backbone through parameter recombination before inference, mitigating storage and computational burdens. Second, we propose a Dynamic Boundary-Aware (DBA) method to generate dynamic pseudo-features for classifier calibration to address the classifier bias. Extensive experiments demonstrate that our approach achieves state-of-the-art performance, particularly showcasing significant improvements of 4.82% and 5.92% on TinyImageNet and ImageNet-Subset, respectively.

Chest X-ray (CXR) images have been widely adopted in clinical care and pathological diagnosis in recent years. Some advanced methods on CXR classification task achieve impressive performance by training the model statically. However, in the real clinical environment, the model needs to learn continually and this can be viewed as a domain incremental learning (DIL) problem. Due to large domain gaps, DIL is faced with catastrophic forgetting. Therefore, in this paper, we propose a Cross-domain invariant feature absorption and Domain-specific feature retention (CaD) framework. To be specific, we adopt a Cross-domain Invariant Feature Absorption (CIFA) module to learn the domain invariant knowledge and a Domain-Specific Feature Retention (DSFR) module to learn the domain-specific knowledge. The CIFA module contains the C(lass)-adapter and an absorbing strategy is used to fuse the common features among different domains. The DSFR module contains the D(omain)-adapter for each domain and it connects to the network in parallel independently to prevent forgetting. A multi-label contrastive loss (MLCL) is used in the training process and improves the class distinctiveness within each domain. We leverage publicly available large-scale datasets to simulate domain incremental learning scenarios, extensive experimental results substantiate the effectiveness of our proposed methods and it has reached state-of-the-art performance.

Online task-free continual learning (OTFCL) is a more challenging variant of continual learning that emphasizes the gradual shift of task boundaries and learning in an online mode. Existing methods rely on a memory buffer of old samples to prevent forgetting. However, the use of memory buffers not only raises privacy concerns but also hinders the efficient learning of new samples. To address this problem, we propose a novel framework called I$^{2}$CANSAY that gets rid of the dependence on memory buffers and efficiently learns the knowledge of new data from one-shot samples. Concretely, our framework comprises two main modules. Firstly, theInter-Class Analogical Augmentation(ICAN) module generates diverse pseudo-features for old classes based on the inter-class analogy of feature distributions for different new classes, serving as a substitute for the memory buffer. Secondly, theIntra-Class Significance Analysis(ISAY) module analyzes the significance of attributes for each class via its distribution standard deviation, and generates an importance vector as a correction bias for the linear classifier, thereby enhancing the capability of learning from new samples. We run our experiments on four popular image classification datasets: CoRe50, CIFAR-10, CIFAR-100, and CUB-200, our approach outperforms the prior state-of-the-art by a large margin.

Domain incremental object detection (DIOD) aims to gradually learn a unified object detection model from a dataset stream composed of different domains, achieving good performance in all encountered domains. The most critical obstacle to this goal is the catastrophic forgetting problem, where the performance of the model improves rapidly in new domains but deteriorates sharply in old ones after a few sessions. To address this problem, we propose a non-exemplar DIOD method named learning domain bias (LDB), which learns domain bias independently at each new session, avoiding saving examples from old domains. Concretely, a base model is first obtained through training during session 1. Then, LDB freezes the weights of the base model and trains individual domain bias for each new incoming domain, adapting the base model to the distribution of new domains. At test time, since the domain ID is unknown, we propose a domain selector based on nearest mean classifier (NMC), which selects the most appropriate domain bias for a test image. Extensive experimental evaluations on two series of datasets demonstrate the effectiveness of the proposed LDB method in achieving high accuracy on new and old domain datasets. The code is available at https://github.com/SONGX1997/LDB.

Recent studies have demonstrated the potency of leveraging prompts in Transformers for continual learning (CL). Nevertheless, employing a discrete key-prompt bottleneck can lead to selection mismatches and inappropriate prompt associations during testing. Furthermore, this approach hinders adaptive prompting due to the lack of shareability among nearly identical instances at more granular level. To address these challenges, we introduce the Evolving Parameterized Prompt Memory (EvoPrompt), a novel method involving adaptive and continuous prompting attached to pre-trained Vision Transformer (ViT), conditioned on specific instance. We formulate a continuous prompt function as a neural bottleneck and encode the collection of prompts on network weights. We establish a paired prompt memory system consisting of a stable reference and a flexible working prompt memory. Inspired by linear mode connectivity, we progressively fuse the working prompt memory and reference prompt memory during inter-task periods, resulting in continually evolved prompt memory. This fusion involves aligning functionally equivalent prompts using optimal transport and aggregating them in parameter space with an adjustable bias based on prompt node attribution. Additionally, to enhance backward compatibility, we propose compositional classifier initialization, which leverages prior prototypes from pre-trained models to guide the initialization of new classifiers in a subspace-aware manner. Comprehensive experiments validate that our approach achieves state-of-the-art performance in both class and domain incremental learning scenarios.

In this paper, we focus on a challenging Online Task-Free Class Incremental Learning (OTFCIL) problem. Dif-ferent from the existing methods that continuously learn the feature space from data streams, we propose a novel compute-and-align paradigm for the OTFCIL. It first com-putes an optimal geometry, i.e., the class prototype distri-bution, for classifying existing classes and updates it when new classes emerge, and then trains a DNN model by aligning its feature space to the optimal geometry. To this end, we develop a novel Dynamic Neural Collapse (DNC) algorithm to compute and update the optimal geometry. The DNC ex-pands the geometry when new classes emerge without loss of the geometry optimality and guarantees the drift distance of old class prototypes with an explicit upper bound. On this basis, we propose a novel DYnamic feature space Self-OrganizatioN (DYSON) method containing three ma-jor components, including 1) a feature extractor, 2) a Dy-namic Feature-Geometry Alignment (DFGA) module aligning the feature space to the optimal geometry computed by DNC and 3) a training-free class-incremental classifier de-rived from the DNC geometry. Experimental comparison results on four benchmark datasets, including CIFAR10, CI-FAR100, CUB200, and CoRe50, demonstrate the efficiency and superiority of the DYSON method. The source code is released at https://github.com/isCDX2IDYSON.

Vision Transformers (ViTs) excel in extracting global information from image patches. However, their inherent limitation lies in effectively extracting information within local regions, hindering their applicability and performance. Particularly, fully supervised pre-trained ViTs, such as Vanilla ViT and CLIP, face the challenge of locality vanishing when adapting to downstream tasks. To address this, we introduce a novel LOcality-aware pRompt lEarning (LORE) method, aiming to improve the adaptation of pre-trained ViTs to downstream tasks. LORE integrates a data-driven Black Box module (i.e., a pre-trained ViT encoder) with a knowledge-driven White Box module. The White Box module is a locality-aware prompt learning mechanism to compensate for ViTs' deficiency in incorporating local information. More specifically, it begins with the design of a Locality Interaction Network (LIN), which treats an image as a neighbor graph and employs graph convolution operations to enhance local relationships among image patches. Subsequently, a Knowledge-Locality Attention (KLA) mechanism is proposed to capture critical local regions from images, learning Knowledge-Locality (K-L) prototypes utilizing relevant semantic knowledge. Afterwards, K-L prototypes guide the training of a Prompt Generator (PG) to generate locality-aware prompts for images. The locality-aware prompts, aggregating crucial local information, serve as additional input for our Black Box module. Combining pre-trained ViTs with our locality-aware prompt learning mechanism, our Black-White Box model enables the capture of both global and local information, facilitating effective downstream task adaptation. Experimental evaluations across four downstream tasks demonstrate the effectiveness and superiority of our LORE.

Diffusion models have revolutionized customized text-to-image generation, allowing for efficient synthesis of photos from personal data with textual descriptions. However, these advancements bring forth risks including privacy breaches and unauthorized replication of artworks. Previous researches primarily center around using “prompt-specific methods” to generate adversarial examples to protect personal images, yet the effectiveness of existing methods is hindered by constrained adaptability to different prompts. In this paper, we introduce a Prompt-Agnostic Adversarial Perturbation (PAP) method for customized diffusion models. PAP first models the prompt distribution using a Laplace Approximation, and then produces prompt-agnostic perturbations by maximizing a disturbance expectation based on the modeled distribution. This approach effectively tackles the prompt-agnostic attacks, leading to improved defense stability. Extensive experiments in face privacy and artistic style protection, demonstrate the superior generalization of our method in comparison to existing techniques.

Object detection with the capacity to incrementally adapt to new domains is a crucial yet relatively under-explored research topic. The catastrophic forgetting problem presents a significant challenge to achieve this goal, where the model’s performance improves quickly in new conditions but deteriorates sharply in old ones after several incremental learning sessions. Drawing on recent discoveries in visual memories of the human brain, we introduce the Topology-Preserving Domain Incremental Object Detection (TP-DIOD) approach, which aims to address the catastrophic forgetting problem by extracting the topological structure of the feature space learned by the Convolutional Neural Network (CNN) model and preserving this topology during the subsequent incremental learning sessions. Specifically, we model the feature space topology using the self-organizing map (SOM) and construct an anchor image set based on the centroid vectors of the SOM nodes to memorize the feature space topology. We then develop the anchor loss function to penalize the topological changes of the feature space during the subsequent incremental learning sessions. Experimental evaluations on two sets of datasets demonstrate the effectiveness of the proposed TP-DIOD method in mitigating the catastrophic forgetting problem and achieving high accuracy on both old and new domain datasets.

Benefiting from the rich information provided by different modalities, multi-modal tracking has shown significant improvements compared to single-modal tracking. However, in practical applications, multi-modal tracking still faces two major challenges. Firstly, it is crucial to effectively integrate the complementary information from different modalities in order to improve tracking performance. Secondly, as trackers are often deployed in dynamic environments, it is difficult to ensure complete multi-modal data. Thus, handling modal-missing issues is essential to achieve robust and reliable tracking. To address these challenges, this paper proposes a Knowledge Synergy Network (KSNet) that integrates multi-modal features into a comprehensive representation and incorporates a modal compensation mechanism to handle modal-missing issues. With this framework, a multi-modal tracker (KSTrack) is built and trained using multi-modal data. KSTrack is capable of handling both complete and incomplete multi-modal data during inference. Comprehensive experiments on four large-scale RGB-Thermal (RGB-T) and RGB-Depth (RGB-D) benchmarks show that KSTrack surpasses state-of-the-art multi-modal trackers when using multi-modal data and outperforms single-modal trackers by a large margin when using single-modal data.

FSCIL (Few-shot class-incremental learning) is a prominent research topic in the ML community. It faces two significant challenges: forgetting old class knowledge and overfitting to limited new class training examples. In this paper, we present a novel FSCIL approach inspired by the human brain’s analogical learning mechanism, which enables human beings to form knowledge about a target domain from the knowledge of the source domains that are analogical to the target in some aspects. The proposed analogical learning-based FSCIL (ALFSCIL) method consists of two major components: new class classifier constructor (NCCC) and Meta-Analogical training (MAT). The NCCC module utilizes a multi-head cross-attention transformer to compute analogies between new and old classes, generating new class classifiers by blending old class classifiers based on the computed analogies. The MAT module updates the parameters of the CNN feature extractor, the NCCC module, and the knowledge for each encountered class after each round of the FSCIL session. We turn the optimization process into a bi-level optimization problem(BOP) whose theoretical analysis proves the stability and plasticity of our proposed model. Experimental evaluations reveal that this proposed ALFSCIL method achieves the SOTA performance accuracies on three benchmark datasets: CIFAR100, miniImageNet, and CUB200.

In the context of incremental class learning, deep neural networks are prone to catastrophic forgetting, where the accuracy of old classes declines substantially as new knowledge is learned. While recent studies have sought to address this issue, most approaches suffer from either the stability-plasticity dilemma or excessive computational and parameter requirements. To tackle these challenges, we propose a novel framework, the Diverse Knowledge Transfer Transformer (DKT), which incorporates two knowledge transfer mechanisms that use attention mechanisms to transfer both task-specific and task-general knowledge to the current task, along with a duplex classifier to address the stability-plasticity dilemma. Additionally, we design a loss function that clusters similar categories and discriminates between old and new tasks in the feature space. The proposed method requires only a small number of extra parameters, which are negligible in comparison to the increasing number of tasks. We perform extensive experiments on CIFAR100, ImageNet100, and ImageNet1000 datasets, which demonstrate that our method outperforms other competitive methods and achieves state-of-the-art performance. Our source code is available at https://github.com/MIVXJTU/DKT.

Current class-incremental learning research mainly focuses on single-label classification tasks while multi-label class-incremental learning (MLCIL) with more practical application scenarios is rarely studied. Although there have been many anti-forgetting methods to solve the problem of catastrophic forgetting in single-label class-incremental learning, these methods have difficulty in solving the MLCIL problem due to label absence and information dilution problems. To solve these problems, we propose a Knowledge Restore and Transfer (KRT) framework containing two key components. First, a dynamic pseudo-label (DPL) module is proposed to solve the label absence problem by restoring the knowledge of old classes to the new data. Second, an incremental cross-attention (ICA) module is designed to maintain and transfer the old knowledge to solve the information dilution problem. Comprehensive experimental results on MS-COCO and PASCAL VOC datasets demonstrate the effectiveness of our method for improving recognition performance and mitigating forgetting on multi-label class-incremental learning tasks. The source code is available at https://gith.ub.com/witdsl/KRT-MLCIL.

In the Class-Incremental Learning (CIL) task, rehearsal-based approaches have received a lot of attention recently. However, storing old class samples is often infeasible in application scenarios where device memory is insufficient or data privacy is important. Therefore, it is necessary to rethink Non-Exemplar Class-Incremental Learning (NECIL). In this paper, we propose a novel NECIL method named POLO with an adaPtive Old cLass recOnstruction mechanism, in which a density-based prototype reinforcement method (DBR), a topology-correction prototype adaptation method (TPA), and an adaptive prototype augmentation method (APA) are designed to reconstruct pseudo features of old classes in new incremental sessions. Specifically, the DBR focuses on the low-density features to maintain the model's discriminative ability for old classes. Afterward, the TPA is designed to adapt old class prototypes to new feature spaces in the incremental learning process. Finally, the APA is developed to further adapt pseudo feature spaces of old classes to new feature spaces. Experimental evaluations on four benchmark datasets demonstrate the effectiveness of our proposed method over the state-of-the-art NECIL methods.

The data association problem of multi-object tracking (MOT) aims to assign IDentity (ID) labels to detections and infer a complete trajectory for each target. Most existing methods assume that each detection corresponds to a unique target and thus cannot handle situations when multiple targets occur in a single detection due to detection failure in crowded scenes. To relax this strong assumption for practical applications, we formulate the MOT as a Maximizing An Identity-Quantity Posterior (MAIQP) problem on the basis of associating each detection with an identity and a quantity characteristic and then provide solutions to tackle two key problems arising. Firstly, a local target quantification module is introduced to count the number of targets within one detection. Secondly, we propose an identity-quantity harmony mechanism to reconcile the two characteristics. On this basis, we develop a novel Identity-Quantity HArmonic Tracking (IQHAT) framework that allows assigning multiple ID labels to detections containing several targets. Through extensive experimental evaluations on five benchmark datasets, we demonstrate the superiority of the proposed method.

This paper focuses on the unsupervised domain adaptation problem for video-based crowd counting, in which we use labeled data as source domain and unlabelled video data as target domain. It is challenging as there is a huge gap between the source and the target domain and no annotations of samples are available in the target domain. The key issue is how to utilize unlabelled videos in the target domain for knowledge learning and transferring from the source domain. To tackle this problem, we propose a novel Error-aware Density Isomorphism REConstruction Network (EDIREC-Net) for cross-domain crowd counting. EDIREC-Net jointly transfers a pre-trained counting model to target domains using a density isomorphism reconstruction objective and models the reconstruction erroneousness by error reasoning. Specifically, as crowd flows in videos are consecutive, the density maps in adjacent frames turn out to be isomorphic. On this basis, we regard the density isomorphism reconstruction error as a self-supervised signal to transfer the pre-trained counting models to different target domains. Moreover, we leverage an estimation-reconstruction consistency to monitor the density reconstruction erroneousness and suppress unreliable density reconstructions during training. Experimental results on four benchmark datasets demonstrate the superiority of the proposed method and ablation studies investigate the efficiency and robustness. The source code is available at https://github.com/GehenHe/EDIREC-Net.

This paper focuses on the Multi-Target Multi-Camera Tracking task (MTMCT), which aims at tracking multiple targets within a multi-camera network. As the trajectory of each target is inherently split into multiple sub-trajectories (namely local tracklets) in a multi-camera network, a major challenge of MTMCT is how to accurately match the local tracklets generated within each camera across different cameras and generate a complete global trajectory for each target, i.e., the cross-camera tracklet matching problem. We solve the cross-camera tracklet matching problem by TRACklet-to-Target Assignment (TRACTA), and propose the Restricted Non-negative Matrix Factorization (RNMF) algorithm to compute the optimal assignment solution that meets a set of constraints, which should be in force in practice. TRACTA can correct the tracking errors caused by occlusions and missed detections in local tracklets, and produce a complete global trajectory for each target across all the cameras. Moreover, we also develop an analytical way of estimating the total number of targets in the camera network, which plays an important role to compute the tracklet-to-target assignment. Experimental evaluations and ablation studies on four MTMCT benchmark datasets show the superiority of the proposed TRACTA method.