6 accepted papers
Contrastive Language-Image Pretraining (CLIP) has demonstrated impressive generalization on vision-language tasks by aligning images and short texts. However, its inherent 77-token length limits the capacity of capturing complex semantics in long captions. Existing long-text adaptations for CLIP typ
In this paper, we identify a common yet challenging issue in image-text matching, i.e., clone negatives: negative image-text pairs that semantically resemble positive pairs, leading to ambiguous and sub-optimal matching outcomes. To tackle this issue, we propose Adaptive Contrastive Learning (AdaCL)…
The navigation task in dense crowds is a key research problem in real-world scenarios. It requires an agent to avoid collisions in dynamic environments and reach the agent's destination, ensuring high accuracy and efficiency in its decisions. Existing methods typically treat pedestrians as rigid bod
'Pre-training + fine-tuning' has been widely used in various downstream tasks. Parameter-efficient fine-tuning (PEFT) has demonstrated higher efficiency and promising performance comapred to traditional full-tuning. The widely used adapter-based and prompt-based methods in PEFT can be uniformly repr…
Finding correspondences between images is essential for many computer vision tasks and sparse matching pipelines have been popular for decades. However matching noise within and between images along with inconsistent keypoint detection frequently degrades the matching performance. We review these pr…
The rapid growth of scientific literature imposes significant challenges for researchers endeavoring to stay updated with the latest advancements in their fields and delve into new areas. We introduce OpenResearcher, an innovative platform that leverages Artificial Intelligence (AI) techniques to ac…