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Supplementary Materials for "WEAKLY SUPERVISED DEFECT LOCALIZATION WITH RESIDUAL FEATURES"

ICIP2025_1674_supplementary-material_Multi_Res_3DGS

Supplementary of IMPROVING OPEN-WORLD CLASS-AGNOSTIC OBJECT DETECTORS VIA FEATURE DISTILLATION WITH STUDENT-AWARE ADAPTATION

Supplementary Materials of "CURVE: CLIP-Utilized Reinforcement learning for Visual image Enhancement via Simple Image Processing" submitted to ICIP 2025

Transformers demonstrate competitive performance in terms of precision on the problem of vision-based object detection. However, they require considerable computational resources due to the quadratic size of the attention weights.

Video outpainting presents a unique challenge of extending the borders while maintaining consistency with the given content. In this paper, we suggest the use of video inpainting models that excel in object flow learning and reconstruction in outpainting rather than solely generating the background as in existing methods. However, directly applying or fine-tuning inpainting models to outpainting has shown to be ineffective, often leading to blurry results.

Collecting high quality data for object detection tasks is challenging due to the inherent subjectivity in labeling the boundaries of an object. This makes it difficult to not only collect consistent annotations across a dataset but also to validate them, as no two annotators are likely to label the same object using the exact same coordinates. These challenges are further compounded when object boundaries are partially visible or blurred, which can be the case in many domains.

Novel view synthesis in 360$^\circ$ scenes from extremely sparse input views is essential for applications like virtual reality and augmented reality. This paper presents a novel framework for novel view synthesis in extremely sparse-view cases. As typical structure-from-motion methods are unable to estimate camera poses in extremely sparse-view cases, we apply DUSt3R to estimate camera poses and generate a dense point cloud.