Large language models (LLMs) are a special class of pretrained language models (PLMs)
obtained by scaling model size, pretraining corpus and computation. LLMs, because of their …

Self-supervised learning (SSL) has recently achieved impressive performance on various
time series tasks. The most prominent advantage of SSL is that it reduces the dependence …

Finding correspondences between images is a fundamental problem in computer vision. In
this paper, we show that correspondence emerges in image diffusion models without any …

In contrast to extensive studies on general vision pre-training for scalable visual
autonomous driving remains seldom explored. Visual autonomous driving applications …

Deep neural networks excel in supervised learning tasks but are constrained by the need for
extensive labeled data. Self-supervised learning emerges as a promising alternative …

Anomaly detection (AD) plays a crucial role in various domains, including cybersecurity,
finance, and healthcare, by identifying patterns or events that deviate from normal behavior …

Backdoor data detection is traditionally studied in an end-to-end supervised learning (SL)
setting. However, recent years have seen the proliferating adoption of self-supervised …

Abstract We present Lazy Visual Grounding for open-vocabulary semantic segmentation,
which decouples unsupervised object mask discovery from object grounding. Plenty of the …

Swift identification of leaf diseases is crucial for sustainable rice farming, a staple grain
consumed globally. The high costs and inefficiencies of manual identification underline the …

The image-level label has prevailed in weakly supervised semantic segmentation tasks due
to its easy availability. Since image-level labels can only indicate the existence or absence …