11 accepted papers
Semi-supervised learning (SSL) based on pseudo-label and consistency has achieved significant success. The core idea behind these methods is to assign sample weights based on pseudo-label probabilities, thereby guiding the model toward biased learning. However, existing research still faces two majo
Instruction data curation is central to improving the instruction-following ability of large language models. However, existing approaches often struggle to simultaneously maintain data quality, diversity, and distributional consistency, largely because they do not explicitly distinguish semantic re…
Text-guided image editing has advanced rapidly with the rise of diffusion models. While flow-based inversion-free methods offer high efficiency by avoiding latent inversion, they often fail to effectively integrate source information, leading to poor background preservation, spatial inconsistencies,
Anticipating and recognizing surgical workflows are critical for intelligent surgical assistance systems. However, existing methods rely on deterministic decision-making, struggling to generalize across the large anatomical and procedural variations inherent in real-world surgeries. In this paper, w…
Surgical phase recognition has become a crucial requirement in laparoscopic surgery, enabling various clinical applications like surgical risk forecasting. Current methods typically identify the surgical phase using individual frame-wise embeddings as the fundamental unit for time modeling. However,…
Latent Diffusion-based Text-to-Image (T2I) is a free image editing tool that typically reverses an image into noise, reconstructs it using its original text prompt, and then generates an edited version under a new target prompt. To preserve unaltered image content, features from the reconstruction a…
One main challenge in time series anomaly detection for industrial IoT lies in the complex spatio-temporal couplings within multivariate data. However, as traditional anomaly detection methods focus on modeling spatial or temporal dependencies independently, resulting in suboptimal representation le…
The application of artificial intelligence technology has greatly enhanced and fortified the safety of energy pipelines, particularly in safeguarding against external threats. The predominant methods involve the integration of intelligent sensors to detect external vibration, enabling the identifica…
Clustering methods achieve performance improvement by jointly learning representation and cluster assignment. However, they do not consider the confidence of pseudo-labels which are not optimal as supervised information, resulting into error accumulation. To address this issue, we propose a Robust P…
Efficiently capturing the complex spatiotemporal representations from large-scale traffic data with uneven data quality remains to be a challenging task. In considering of the dilemma, this work employs the advanced contrastive learning and proposes a novel Spatial-Temporal Synchronous Contextual Co…
Recent approaches based on metric learning have achieved great progress in few-shot learning. However, most of them are limited to image-level representation manners, which fail to properly deal with the intra-class variations and spatial knowledge and thus produce undesirable performance. In this p…