9 accepted papers
Knowledge distillation (KD) has been widely applied in semantic segmentation to compress large models, but conventional approaches primarily preserve in-domain accuracy while neglecting out-of-domain generalization, which is essential under distribution shifts. This limitation becomes more severe wi
Humans often juggle multiple, sometimes conflicting objectives and shift their priorities as circumstances change, rather than following a fixed objective function. In contrast, most computational decision-making and multi-objective RL methods assume static preference weights or a known scalar rewa…
Change detection (CD) holds significant implications for Earth observation, in which pseudo-changes between bitemporal images induced by imaging environmental factors are key challenges. Existing methods mainly regard pseudo-changes as a kind of style shift and alleviate it by transforming bitempora…
Deep learning methods have demonstrated remarkable performance across various communication signal processing tasks. However, most signal classification methods require a substantial amount of labeled samples for training, posing significant challenges in the field of communication signals, as label
"Low illumination significantly impacts the performance of learning-based models trained under well-lit conditions. While current methods mitigate this issue through either image-level enhancement or feature-level adaptation, they often focus solely on the image itself, ignoring how the task-relevan…
Domain adaptive semantic segmentation aims to adapt a model trained on labeled source domain to the unlabeled target domain. Self-training shows competitive potential in this field. Existing methods along this stream mainly focus on selecting reliable predictions on target data as pseudo-labels for…
Unsupervised domain adaptation (UDA) in semantic segmentation transfers the knowledge of the source domain to the target one to improve the adaptability of the segmentation model in the target domain. The need to access labeled source data makes UDA unable to handle adaptation scenarios involving pr…
Image patch matching across different spectral domains is more challenging than in a single spectral domain. We consider the reason is twofold: 1. the weaker discriminative feature learned by conventional methods; 2. the significant appearance difference between two images domains. To tackle these p…
Recent studies on image patch matching are paying more attention on hard sample learning, because easy samples do not contribute much to the network optimization. They have proposed various hard negative sample mining strategies, but very few addressed this problem from the perspective of loss funct…