Training a classifier on web-crawled data demands learning algorithms that are robust to annotation errors and irrelevant examples. This paper builds upon the recent empirical observation that applying unsupervised contrastive learning to noisy, web-crawled datasets yields a feature representation under which the in-distribution (ID) and out-of-distribution (OOD) samples are linearly separable. We show that direct estimation of the separating hyperplane can indeed offer an accurate detection of OOD samples, and yet, surprisingly, this detection does not translate into gains in classification accuracy. Digging deeper into this phenomenon, we discover that the near-perfect detection misses a type of clean examples that are valuable for supervised learning. These examples often represent visually simple images, which are relatively easy to identify as clean examples using standard loss- or distance-based methods despite being poorly separated from the OOD distribution using unsupervised learning. This urges us to propose a hybrid solution that alternates between noise detection using linear separation and a state-of-the-art (SOTA) small-loss approach. When combined with the SOTA algorithm PLS, we substantially improve SOTA results for real-world image classification in the presence of web noise. The code for reproducing the experiments in this paper will be publicly released upon acceptance.
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Cs2K: Class-specific and Class-shared Knowledge Guidance for Incremental Semantic Segmentation
PDF ↗Incremental semantic segmentation endeavors to segment newly encountered classes while maintaining knowledge of old classes. However, existing methods either 1) lack guidance from class-specific knowledge (i.e., old class prototypes), leading to a bias towards new classes, or 2) constrain class-shared knowledge (i.e., old model weights) excessively without discrimination, resulting in a preference for old classes. In this paper, to trade off model performance, we propose the Class-specific and Class-shared Knowledge (Cs2K) guidance for incremental semantic segmentation. Specifically, from the class-specific knowledge aspect, we design a prototype-guided pseudo labeling that exploits feature proximity from prototypes to correct pseudo labels, thereby overcoming catastrophic forgetting. Meanwhile, we develop a prototype-guided class adaptation that aligns class distribution across datasets via learning old augmented prototypes. Moreover, from the class-shared knowledge aspect, we propose a weight-guided selective consolidation to strengthen old memory while maintaining new memory by integrating old and new model weights based on weight importance relative to old classes. Experiments on public datasets demonstrate that our proposed Cs2K significantly improves segmentation performance and is plug-and-play.
In object detection, unsupervised domain adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain. However, UDA's reliance on labeled source data restricts its adaptability in privacy-related scenarios. This study focuses on source-free object detection (SFOD), which adapts a source-trained detector to an unlabeled target domain without using labeled source data. Recent advancements in self-training, particularly with the Mean Teacher (MT) framework, show promise for SFOD deployment. However, the absence of source supervision significantly compromises the stability of these approaches. We identify two primary issues, (1) uncontrollable degradation of the teacher model due to inopportune updates from the student model, and (2) the student model's tendency to replicate errors from incorrect pseudo labels, leading to being trapped in a local optimum. Both factors contribute to a detrimental circular dependency, resulting in rapid performance degradation in recent self-training frameworks. To tackle these challenges, we propose the Dynamic Retraining-Updating (DRU) mechanism, which actively manages the student training and teacher updating processes to achieve co-evolutionary training. Additionally, we introduce Historical Student Loss to mitigate the influence of incorrect pseudo labels. Our method achieves state-of-the-art performance in the SFOD setting on multiple domain adaptation benchmarks, comparable to or even surpassing advanced UDA methods. The code will be released soon.
Multimodal Large Language Models (MLLMs) excel in generating responses based on visual inputs. However, they often suffer from a bias towards generating responses similar to their pretraining corpus, overshadowing the importance of visual information. We treat this bias as a "preference" for pretraining statistics, which hinders the model's grounding in visual input. To mitigate this issue, we propose Bootstrapped Preference Optimization (BPO), which conducts preference learning with datasets containing negative responses bootstrapped from the model itself. Specifically, we propose the following two strategies: 1) using distorted image inputs to the MLLM for eliciting responses that contain signified pretraining bias; 2) leveraging text-based LLM to explicitly inject erroneous but common elements into the original response. Those undesirable responses are paired with original annotated responses from the datasets to construct the preference dataset, which is subsequently utilized to perform preference learning. Our approach effectively suppresses pretrained LLM bias, enabling enhanced grounding in visual inputs. Extensive experimentation demonstrates significant performance improvements across multiple benchmarks, advancing the state-of-the-art in multimodal conversational systems.
We present CLIP-IFDL, a novel image forgery detection and localization (IFDL) model that harnesses the power of Contrastive Language Image Pre-Training (CLIP). However, directly incorporating CLIP in forgery detection poses challenges, given its lack of specific prompts and forgery consciousness. To overcome these challenges, we tailor the CLIP model for forgery detection and localization leveraging a noise-assisted prompt learning framework. This framework comprises instance-aware dual-stream prompt learning and a forgery-enhanced noise adapter. We initially create a pair of learnable prompts as negative-positive samples in place of discrete prompts, then fine-tune these prompts based on each image's features and categories. Additionally, we constrain the text-image similarity between the prompts and their corresponding images to update the prompts. Moreover, We design a forgery-enhanced noise adapter that augments the image encoder's forgery perceptual ability via multi-domain fusion and zero linear layers. By doing so, our method not only extracts pertinent features but also benefits from the generalizability of the open-world CLIP prior. Comprehensive tests indicate that our method outperforms existing ones in terms of accuracy and generalizability while effectively reducing false alarms.
Wildlife ReID involves utilizing visual technology to identify specific individuals of wild animals in different scenarios, holding significant importance for wildlife conservation, ecological research, and environmental monitoring. Existing wildlife ReID methods are predominantly tailored to specific species, exhibiting limited applicability. Although some approaches leverage extensively studied person ReID techniques, they struggle to address the unique challenges posed by wildlife. Therefore, in this paper, we present a unified, multi-species general framework for wildlife ReID. Given that high-frequency information is a consistent representation of unique features in various species, significantly aiding in identifying contours and details such as fur textures, we propose the Adaptive High-Frequency Transformer model with the goal of enhancing high-frequency information learning. To mitigate the inevitable high-frequency interference in the wilderness environment, we introduce an object-aware high-frequency selection strategy to adaptively capture more valuable high-frequency components. Notably, we unify the experimental settings of multiple wildlife datasets for ReID and evaluate our model on diverse wildlife datasets, achieving performance superiority over state-of-the-art ReID methods. In domain generalization scenarios, our approach demonstrates robust generalization to unknown species.
Most Camouflaged Object Detection (COD) methods heavily rely on mask annotations, which are time-consuming and labor-intensive to acquire. Existing weakly-supervised COD approaches exhibit significantly inferior performance compared to fully-supervised methods and struggle to simultaneously support all the existing types of camouflaged object labels, including scribbles, bounding boxes, and points. Even for Segment Anything Model (SAM), it is still problematic to handle the weakly-supervised COD and it typically encounters challenges of prompt compatibility of the scribble labels, extreme response, semantically erroneous response, and unstable feature representations, producing unsatisfactory results in camouflaged scenes. To mitigate these issues, we propose a unified COD framework in this paper, termed SAM-COD, which is capable of supporting arbitrary weakly-supervised labels. Our SAM-COD employs a prompt adapter to handle scribbles as prompts based on SAM. Meanwhile, we introduce response filter and semantic matcher modules to improve the quality of the masks obtained by SAM under COD prompts. To alleviate the negative impacts of inaccurate mask predictions, a new strategy of prompt-adaptive knowledge distillation is utilized to ensure a reliable feature representation. To validate the effectiveness of our approach, we have conducted extensive empirical experiments on three mainstream COD benchmarks. The results demonstrate the superiority of our method against state-of-the-art weakly-supervised and even fully-supervised methods. Our source codes and trained models will be publicly released.
Our brains represent the ever-changing environment with neurons in a highly dynamic fashion. The temporal features of visual pixels in dynamic natural scenes are entangled into the retinal neuronal coding patterns, where effective establishing their intrinsic temporal relationships is crucial. Recent foundation vision models have paved an advanced way of understanding image pixels. Yet, neuronal coding in the brain largely lacks a deep understanding of its alignment with pixels. Most previous studies employ static images or artificial videos derived from static images for emulating more real and complicated stimuli. Despite these simple scenarios effectively help to separate key factors influencing visual coding, complex temporal relationships receive no consideration. To decompose the temporal features of visual coding in natural scenes, here we propose Vi-ST, a spatiotemporal convolutional neural network fed with a self-supervised Vision Transformer (ViT) prior, aimed at unraveling the temporal-based encoding patterns of retinal neuronal populations. The model demonstrates robust predictive performance in generalisation tests. Additionally, through detailed ablation experiments, we demonstrate the significance of each temporal module. Furthermore, we introduce a visual coding evaluation metric designed to integrate temporal considerations and compare the impact of different numbers of neuronal populations on complementary coding. In conclusion, our proposed Vi-ST demonstrates a novel modelling framework for neuronal coding of dynamic visual scenes in the brain, effectively aligning our brain representation of video with neuronal activity.
In recent years, the Whole Slide Image (WSI) classification task has achieved great advancement due to the success of Multiple Instance Learning (MIL). However, MIL-based methods face two limitations: 1) often select the top-ranking instances of a WSI based on different metrics (e.g., attention score) to train the model due to the large resolution of WSIs, which may lead to missing global information; 2) usually consider all instances within a bag to be unordered, which will cause the local context information to be missing. To address the limitations of MIL-based methods, we formulate the WSI classification task as a long sequence classification problem in a weakly supervised setting. We propose a Noise Robust Memory-augmented (Norma) framework that serializes the WSI into an ordered sequence and caches each segment for future reuse in a sequential manner. By applying such paradiam, global and local context information of a WSI can be obtained during training. Furthermore, Normal adopts a Cyclic Training process to eliminate the noise introduced by the WSI-level labe. We obtains state-of-the-art results on CAMELYON-16, TCGA-BRAC and TCGA-LUNG datasets. We will release the code upon acceptance.
Model inversion (MI) attack reconstructs the private training data of a target model given its output, posing a significant threat to deep learning models and data privacy. On one hand, most of existing MI methods focus on searching for latent codes to represent the target identity, yet this iterative optimization-based scheme consumes a huge number of queries to the target model, making it unrealistic especially in black-box scenario. On the other hand, some training-based methods launch an attack through a single forward inference, whereas failing to directly learn high-level mappings from prediction vectors to images. Addressing these limitations, we propose a novel Prediction-to-Image (P2I) method for black-box MI attack. Specifically, we introduce the Prediction Alignment Encoder to map the target model's output prediction into the latent code of StyleGAN. In this way, prediction vector space can be well aligned with the more disentangled latent space, thus establishing a connection between prediction vectors and the semantic facial features. During the attack phase, we further design the Aligned Ensemble Attack scheme to integrate complementary facial attributes of target identity for better reconstruction. Experimental results show that our method outperforms other SOTAs, e.g., compared with RLB-MI, our method improves attack accuracy by 8.5% and reduces query numbers by 99% on dataset CelebA.
In this work, we describe a method for large-scale 3D cell-tracking through a segmentation selection approach. The proposed method is effective at tracking cells across large microscopy datasets on two fronts: (i) It can solve problems containing millions of segmentation instances in terabyte-scale 3D+t datasets; (ii) It achieves competitive results with or without deep learning, bypassing the requirement of 3D annotated data, that is scarce in the fluorescence microscopy field. The proposed method computes cell tracks and segments using a hierarchy of segmentation hypotheses and selects disjoint segments by maximizing the overlap between adjacent frames. We show that this method is the first to achieve state-of-the-art in both nuclei- and membrane-based cell tracking by evaluating it on the 2D epithelial cell benchmark and 3D images from the cell tracking challenge. Furthermore, it has a faster integer linear programming formulation, and the framework is flexible, supporting segmentations from individual off-the-shelf cell segmentation models or their combination as an ensemble. The code is available as supplementary material.
Self-supervised learning (SSL) is a prevalent approach for encoding data representations. Using a pre-trained SSL image encoder and subsequently training a downstream classifier, impressive performance can be achieved on various tasks with very little labeled data. The growing adoption of SSL has led to an increase in security research on SSL encoders and associated Trojan attacks. Trojan attacks embedded in SSL encoders can operate covertly, spreading across multiple users and devices. The presence of backdoor behavior in Trojaned encoders can inadvertently be inherited by downstream classifiers, making it even more difficult to detect and mitigate the threat. Although current Trojan detection methods in supervised learning can potentially safeguard SSL downstream classifiers, identifying and addressing triggers in the SSL encoder before its widespread dissemination is a challenging task. This challenge arises because downstream tasks might be unknown, dataset labels may be unavailable, and the original unlabeled training dataset might be inaccessible during Trojan detection in SSL encoders. We introduce \textbf{SSL-Cleanse} as a solution to identify and mitigate backdoor threats in SSL encoders. We evaluated SSL-Cleanse on various datasets using 1200 encoders, achieving an average detection success rate of $82.2\%$ on ImageNet-100. After mitigating backdoors, on average, backdoored encoders achieve $0.3\%$ attack success rate without great accuracy loss, proving the effectiveness of SSL-Cleanse.
Background Adaptation with Residual Modeling for Exemplar-Free Class-Incremental Semantic Segmentation
PDF ↗Class Incremental Semantic Segmentation~(CISS), within Incremental Learning for semantic segmentation, targets segmenting new categories while reducing the catastrophic forgetting on the old categories.Besides, background shifting, where the background category changes constantly in each step, is a special challenge for CISS. Current methods with a shared background classifier struggle to keep up with these changes, leading to decreased stability in background predictions and reduced accuracy of segmentation. For this special challenge, we designed a novel background adaptation mechanism, which explicitly models the background residual rather than the background itself in each step, and aggregates these residuals to represent the evolving background. Therefore, the background adaptation mechanism ensures the stability of previous background classifiers, while enabling the model to concentrate on the easy-learned residuals from the additional channel, which enhances background discernment for better prediction of novel categories. To precisely optimize the background adaptation mechanism, we propose Pseudo Background Binary Cross-Entropy loss and Background Adaptation losses, which amplify the adaptation effect. Group Knowledge Distillation and Background Feature Distillation strategies are designed to prevent forgetting old categories. Our approach, evaluated across various incremental scenarios on Pascal VOC 2012 and ADE20K datasets, outperforms prior exemplar-free state-of-the-art methods with mIoU of 3.0% in VOC 10-1 and 2.0% in ADE 100-5, notably enhancing the accuracy of new classes while mitigating catastrophic forgetting. Code is available in supplementary material.
In this paper, we present a learning-based framework for improved sparse depth video completion. Given a sparse depth map and a color image, our approach makes a cost volume of a certain viewpoint that constructed on depth hypothesis planes. To effectively handle sequential cost volumes of the multiple viewpoints, we introduce a learning-based cost volume fusion framework, namely RayFusion, that effectively leverages the attention mechanism for each pair of overlapped rays in cost volumes. As a result of leveraging feature statistics accumulated over time, our proposed framework consistently outperforms or rivals state-of-the-art approaches on diverse indoor and outdoor datasets, including the KITTI Depth Completion benchmark, VOID Depth Completion benchmark, and ScanNetV2 dataset, using 94.5% fewer network parameters than the state-of-the-art approach, LRRU.
Accurate tracking of a user’s body pose while wearing a virtual reality (VR), augmented reality (AR) or mixed reality (MR) headset is a prerequisite for authentic self-expression, natural social presence, and intuitive user interfaces. Existing body tracking approaches on VR/AR devices are either under-constrained, e.g., attempting to infer full body pose from only headset and controller pose, or require impractical hardware setups that place cameras far from a user’s face to improve body visibility. In this paper, we present the first controllerless egocentric body tracking solution that runs on an actual VR device using the same cameras that are used for SLAM tracking. We propose a novel egocentric tracking architecture that models the temporal history of body motion using multi-view latent features. Furthermore, we release the first large-scale real-image dataset for egocentric body tracking, EgoBody3M, with a realistic VR headset configuration and diverse subjects and motions. Benchmarks on the dataset shows that our approach outperforms other state-of-the-art methods in both accuracy and smoothness of the resulting motion. We perform ablation studies on our model choices and demonstrate the method running in realtime on a VR headset. Our dataset with more than 30 hours of recordings and 3 million frames will be made publicly available.
Most current single image-deraining (SID) methods are based on the Transformer, which brings global modeling capabilities and is critical for high-quality reconstruction. However, their architectures only consider constructing long-range dependencies from the spatial domain, which suffers from a significant computational burden to keep effectiveness. Besides, these methods either overlook negative sample information in the optimization pipeline or underutilize the rain streak characteristics present in the negative ones. To tackle these problems, we propose a Frequency-Aware Deraining Transformer Framework (FADformer) that fully captures frequency domain features to achieve efficient rain removal. Specifically, we first construct the FADBlock, which comprises the Fused Fourier Convolution Mixer (FFCM) and Prior-Gated Feed-forward Network (PGFN). Unlike self-attention mechanisms, the FFCM exclusively conducts convolution operations in both spatial and frequency domains, endowing it with local-global capturing capabilities and efficiency. Simultaneously, the PGFN introduces residue channel prior in a gate-controlled manner to both enhance local details and retain the structure of features. Furthermore, we introduce a Frequency-domain Contrastive Regularization (FCR) during the training phase. The FCR facilitates contrastive learning in the frequency domain, enhancing the contribution of rain streak patterns in negative samples to improve performance. Extensive experiments on synthetic and real-world datasets show that the proposed method significantly outperforms the state-of-the-art approaches. We will release the code soon after the paper is accepted.
When deploying a semantic segmentation model into the real world, it will inevitably encounter semantic classes that were not seen during training. Therefore, to ensure a safe deployment of such systems, it is crucial to accurately evaluate and improve their anomaly segmentation capabilities. However, acquiring and labelling semantic segmentation data is expensive and unanticipated conditions are long-tail and potentially hazardous. Indeed, existing anomaly segmentation datasets capture a limited number of anomalies, lack realism or have strong domain shifts. In this paper, we propose the Placing Objects in Context (POC) pipeline to realistically add any object into any image via diffusion models. POC can be used to easily extend any dataset with an arbitrary number of objects. In our experiments, we present different anomaly segmentation datasets based on POC-generated data and show that POC can improve the performance of recent state-of-the-art anomaly fine-tuning methods across several standardized benchmarks. POC is also effective for learning new classes. For example, we utilize it to augment Cityscapes samples by incorporating a subset of Pascal classes and demonstrate that models trained on such data achieve comparable performance to the Pascal-trained baseline. This corroborates the low synth2real gap of models trained on POC-generated images.
Human Motion Forecasting in Dynamic Domain Shifts: A Homeostatic Continual Test-time Adaptation Framework
PDF ↗Existing motion forecasting models, while making progress, struggle to bridge the gap between the source and target domains. Recent solutions often rely on an unrealistic assumption that the target domain remains stationary. Due to the ever-changing environment, however, the real-world test distribution may experience ongoing/continual shifts over time, leading to catastrophic forgetting and error accumulation when adapting to evolving domains. To solve these challenges, this work introduces HoCoTTA, a framework for homeostatic continual test-time adaptation. It aligns with the knowledge distillation and parameter isolation paradigm, enabling the identification of domain-invariant and domain-specific knowledge, where the former is shared (to be retained) in continual TTA across domains, while the latter needs to be updated. Specifically, we propose a multi-domain homeostasis assessment to estimate the uncertainty of the current model parameter when faced with novel-domain samples. Then, the Fisher information matrix is computed to measure the parameter sensitivity, with larger indicating the domain-sensitive parameter, and vice versa. Moreover, we propose an isolated parameter optimization strategy to update those domain-specific parameters to adapt to the new-domain, while preserving the invariant ones. In our experimental result, HoCoTTA outperforms the state-of-the-art approaches on several benchmarks, especially excelling in addressing continuous domain drifts, achieving a large improvement. More details and results could be found in the supplementary material.
As deep neural networks evolve from convolutional neural networks (ConvNets) to advanced vision transformers (ViTs), there is an increased need to eliminate redundant data for faster processing without compromising accuracy. Previous methods are often architecture-specific or necessitate re-training, restricting their applicability with frequent model updates. To solve this, we first introduce a novel property of lightweight ConvNets: their ability to identify key discriminative patch regions in images, irrespective of model's final accuracy or size. We demonstrate that fully-connected layers are the primary bottleneck for ConvNets performance, and their suppression with simple weight recalibration markedly enhances discriminative patch localization performance. Using this insight, we introduce PaPr, a method for substantially pruning redundant patches with minimal accuracy loss using lightweight ConvNets across a variety of deep learning architectures, including ViTs, ConvNets, and hybrid transformers, without any re-training. Moreover, the simple early-stage one-step patch pruning with PaPr enhances existing patch reduction methods. Through extensive testing on diverse architectures, PaPr achieves significantly higher accuracy over state-of-the-art patch reduction methods with similar FLOP count reduction. More specifically, PaPr reduces about 70% of redundant patches in videos with less than 0.8% drop in accuracy, and up to 3.7x FLOPs reduction, which is a 15% more reduction with 2.5% higher accuracy. Code will be released.
Flexible Distribution Alignment: Towards Long-tailed Semi-supervised Learning with Proper Calibration
PDF ↗Long-tailed semi-supervised learning (LTSSL) represents a practical scenario for semi-supervised applications, challenged by skewed labeled distributions that bias classifiers. This problem is often aggravated by discrepancies between labeled and unlabeled class distributions, leading to biased pseudo-labels, neglect of rare classes, and poorly calibrated probabilities. To address these issues, we introduce Flexible Distribution Alignment (FlexDA), a novel adaptive logit-adjusted loss framework designed to dynamically estimate and align predictions with the actual distribution of unlabeled data and achieve a balanced classifier by the end of training. FlexDA is further enhanced by a distillation-based consistency loss, promoting fair data usage across classes and effectively leveraging underconfident samples. This method, encapsulated in ADELLO (Align and Distill Everything All at Once), proves robust against label shift, significantly improves model calibration in LTSSL contexts, and surpasses previous state-of-of-art approaches across multiple benchmarks, including CIFAR100-LT, STL10-LT, and ImageNet127, addressing class imbalance challenges in semi-supervised learning. Our code will be made available upon paper acceptance.