6 accepted papers
Information Bottleneck (IB) Theory is renowned for its ability to learn simple, compact, and effective data representations. In multi-modal clustering, IB theory effectively eliminates interfering redundancy and noise from multi-modal data, while maximally preserving the discriminative information.…
Contrastive Language-Image Pre-training (CLIP) exhibits strong zero-shot classification ability on image-level tasks, leading to the research to adapt CLIP for open-vocabulary semantic segmentation without training. The key is to improve spatial representation of image-level CLIP, such as replacing…
To extract spatial information, depth estimation using conventional echo-based methods typically employs models with encoder-decoder architectures, such as UNet. However, these methods may face challenges in extracting fine details from echo waveforms and handling multi-scale feature extraction with…
As an important part of intelligent manufacturing, pixel-level surface defect detection (SDD) aims to locate defect areas through mask prediction. Previous methods adopt the image-independent static convolution to indiscriminately classify per-pixel features for mask prediction, which leads to subop…
Out-of-distribution (OOD) detection methods often exploit auxiliary outliers to train model identifying OOD samples especially discovering challenging outliers from auxiliary outliers dataset to improve OOD detection. However they may still face limitations in effectively distinguishing between the…
Convolutional Neural Network (CNN) based methods generally take crowd counting as a regression task by outputting crowd densities. They learn the mapping between image contents and crowd density distributions. Though having achieved promising results, these data-driven counting networks are prone to…