10 accepted papers
Modern multi-view clustering (MVC) is dominated by two paradigms: multi-view fusion and pseudo-label-guided learning. Pseudo-labeling methods can suffer from confirmation bias; their reliance on a fixed-granularity supervision from an initial clustering can cause learned embeddings to drift from the
The Transformer architecture has revolutionized various fields since it was proposed, where positional encoding plays an essential role in effectively capturing sequential order and context. Therefore, Rotary Positional Encoding (RoPE) was proposed to alleviate these issues, which integrates positio…
Chain-of-Thought (CoT) prompting improves the reasoning performance of large language models (LLMs) by encouraging step-by-step thinking. However, CoT-based methods depend on intermediate reasoning steps, which limits scalability and generalization. Recent work explores recursive reasoning, where L…
With the rapid advancement of Visual Place Recognition (VPR) systems, their unauthorized use on social media images enables monitoring of individuals' daily movements, posing serious privacy risks. However, privacy protection for addressing these risks in VPR systems remains an underexplored area. W…
Visual place recognition (VPR) is a fundamental task for many applications such as robot localization and augmented reality. Recently, the hierarchical VPR methods have received considerable attention due to the trade-off between accuracy and efficiency. They usually first use global features to ret…
Visual place recognition (VPR) is an essential task for multiple applications such as augmented reality and robot localization. Over the past decade, mainstream methods in the VPR area have been to use feature representation based on global aggregation, as exemplified by NetVLAD. These features are…
Recent studies show that vision models pre-trained in generic visual learning tasks with large-scale data can provide useful feature representations for a wide range of visual perception problems. However, few attempts have been made to exploit pre-trained foundation models in visual place recogniti…
Visual place recognition (VPR) is one of the research hotspots in robotics, which uses visual information to locate robots. Recently, the hierarchical two-stage VPR methods have become popular in this field due to the trade-off between accuracy and efficiency. These methods retrieve the top-k candid…
Recently, techniques utilizing frequency-based methods have gained significant attention, as they exhibit exceptional restoration capabilities for detail and structure in video super-resolution tasks. However, most of these frequency-based methods mainly have three major limitations: 1) insufficient…
Frequency-based methods have recently received much attention due to their impressive restoration of detail and structure in single image super-resolution (SISR). However, most of these methods mainly use frequency information as auxiliary means but ignore exploring the correlations and pixel distri…