IEEE transactions on pattern analysis and machine intelligence最新文献

Due to the substantial domain gaps in Remote Sensing (RS) images that are characterized by variabilities such as location, wavelength, and sensor type, Remote Sensing Domain Generalization (RSDG) has emerged as a critical and valuable research frontier, focusing on developing models that generalize effectively across diverse scenarios. However, research in this area remains underexplored: (1) Current cross-domain methods primarily focus on Domain Adaptation (DA), which adapts models to predefined domains rather than to unseen ones; (2) Few studies target the RSDG issue, especially for semantic segmentation tasks. Existing related models are developed for specific unknown domains, struggling with issues of underfitting on other unseen scenarios; (3) Existing RS foundation models tend to prioritize in-domain performance over cross-domain generalization. To this end, we introduce the first vision foundation model for RSDG semantic segmentation, CrossEarth. CrossEarth demonstrates strong cross-domain generalization through a specially designed data-level Earth-Style Injection pipeline and a model-level Multi-Task Training pipeline. In addition, for the semantic segmentation task, we have curated an RSDG benchmark comprising 32 semantic segmentation scenarios across various regions, spectral bands, platforms, and climates, providing comprehensive evaluations of the generalizability of future RSDG models. Extensive experiments on this collection demonstrate the superiority of CrossEarth over existing state-of-the-art methods.

Lifelong person Re-IDentification (L-ReID) exploits sequentially collected data to continuously train and update a ReID model, focusing on the overall performance of all data. Its main challenge is to avoid the catastrophic forgetting problem of old knowledge while training on new data. Existing L-ReID methods typically re-extract new features for all historical gallery images for inference after each update, known as "re-indexing". However, historical gallery data typically suffers from direct saving due to the data privacy issue and the high re-indexing costs for large-scale gallery images. As a result, it inevitably leads to incompatible retrieval between query features extracted by the updated model and gallery features extracted by those before the update, greatly impairing the re-identification performance. To tackle the above issue, this paper focuses on a new task called Re-index Free Lifelong person Re-IDentification (RFL-ReID), which requires performing lifelong person re-identification without re-indexing historical gallery images. Therefore, RFL-ReID is more challenging than L-ReID, requiring continuous learning and balancing new and old knowledge in diverse streaming data, and making the features output by the new and old models compatible with each other. To this end, we propose a Bidirectional Continuous Compatible Representation (Bi-C²R) framework to continuously update the gallery features extracted by the old model to perform efficient L-ReID in a compatible manner. Specifically, a bidirectional compatible transfer network is first designed to bridge the relationship between new and old knowledge and continuously update the old gallery features to the new feature space after the updating. Secondly, a bidirectional compatible distillation module and a bidirectional anti-forgetting distillation model are designed to balance the compatibility between the new and old knowledge in dual feature spaces. Finally, a feature-level exponential moving average strategy is designed to adaptively fill the diverse knowledge gaps between different data domains. Finally, we verify our proposed Bi-C²R method through theoretical analysis and extensive experiments on multiple benchmarks, which demonstrate that the proposed method can achieve leading performance on both the introduced RFL-ReID task and the traditional L-ReID task.

Deep neural networks possess remarkable learning capabilities and expressive power, but this makes them vulnerable to overfitting, especially when they encounter mislabeled data. A notable phenomenon called the memorization effect occurs when networks first learn the correctly labeled data and later memorize the mislabeled instances. While early stopping can mitigate overfitting, it doesn't entirely prevent networks from adapting to incorrect labels during the initial training phases, which can result in losing valuable insights from accurate data. Moreover, early stopping cannot rectify the mistakes caused by mislabeled inputs, underscoring the need for improved strategies. In this paper, we introduce an innovative mechanism for continuous review and timely correction of learned knowledge. Our approach allows the network to repeatedly revisit and reinforce correct information while promptly addressing any inaccuracies stemming from mislabeled data. We present a novel method called self-not-true-distillation (SNTD). This technique employs self-distillation, where the network from previous training iterations acts as a teacher, guiding the current network to review and solidify its understanding of accurate labels. Crucially, SNTD masks the true class label in the logits during this process, concentrating on the non-true classes to correct any erroneous knowledge that may have been acquired. We also recognize that different data classes follow distinct learning trajectories. A single teacher network might struggle to effectively guide the learning of all classes at once, which necessitates selecting different teacher networks for each specific class. Additionally, the influence of the teacher network's guidance varies throughout the training process. To address these challenges, we propose SNTD+, which integrates a class-wise distillation strategy along with a dynamic weight adjustment mechanism. Together, these enhancements significantly bolster SNTD's robustness in tackling complex scenarios characterized by label noise.

Spike camera is an emerging bio-inspired vision sensor with ultra-high temporal resolution. It records scenes by accumulating photons and outputting binary spike streams. Optical flow estimation aims to estimate pixel-level correspondences between different moments, describing motion information along time, which is a key task of spike camera. High-quality optical flow is important since motion information is a foundation for analyzing spikes. However, extracting stable light-intensity information from spikes is difficult due to the randomness of binary spikes. Besides, the continuity of spikes can offer contextual information for optical flow. In this paper, we propose a network Spike2Flow++ to estimate optical flow for spike camera. In Spike2Flow++, we propose a differential of spike firing time (DSFT) to represent information in binary spikes. Moreover, we propose a dual DSFT representation and a dual correlation construction to extract stable light-intensity information for reliable correlations. To use the continuity of spikes as motion contextual information, we propose a joint correlation decoding (JCD) that jointly estimates a series of flow fields. To adaptively fuse different motions in JCD, we propose a global motion bank aggregation to construct an information bank for all motions and adaptively extract contexts from the bank for each iteration during recurrent decoding of each motion. To train and evaluate our network, we construct a real scene with spikes and flow++ (RSSF++) based on real-world scenes. Experiments demonstrate that our Spike2Flow++ achieves state-of-the-art performance on RSSF++, photo-realistic high-speed motion (PHM), and real-captured data.