6 accepted papers
Label Distribution Learning (LDL) is a groundbreaking paradigm for addressing the task with label ambiguity. Subjectivity in annotating label description degrees often leads to imbalanced label distribution. Existing approaches either adopt representation alignment or decoupling strategies to solve
Label Distribution Learning (LDL) effectively addresses label ambiguity by modeling the degree to which each label describes an instance. A key challenge in LDL is Label Enhancement (LE): recovering label distributions from logical labels. Existing LE methods typically treat logical labels as superv
We present MeanCache, a training-free caching framework for efficient Flow Matching inference. Existing caching methods reduce redundant computation but typically rely on instantaneous velocity information (e.g., feature caching), which often leads to severe trajectory deviations and error accumulat…
Label Distribution Learning (LDL) has been successfully implemented in numerous practical applications. However, the imbalance in label distributions presents a significant challenge due to the substantial variation in annotation information. To tackle this issue, we introduce Decoupled Imbalance La
We present LeMiCa, a training-free and efficient acceleration framework for diffusion-based video generation. While existing caching strategies primarily focus on reducing local heuristic errors, they often overlook the accumulation of global errors, leading to noticeable content degradation between…
Federated Learning (FL) is considered a new paradigm of privacy-preserving machine learning since the server trains a machine learning model in a distributed way without collecting clients’ raw data but only local models. However, recent studies show that FL suffers inference attacks. Sensitive info…