Today's deep learning methods focus on how to design the most appropriate objective
functions so that the prediction results of the model can be closest to the ground truth …

Abstract Contrastive Vision-Language Pre-training, known as CLIP, has provided a new
paradigm for learning visual representations using large-scale image-text pairs. It shows …

Masked Autoencoders (MAE) have shown great potentials in self-supervised pre-training for
language and 2D image transformers. However, it still remains an open question on how to …

Transformer, an attention-based encoder–decoder model, has already revolutionized the
field of natural language processing (NLP). Inspired by such significant achievements, some …

In recent years, deep learning techniques for processing 3D point cloud data have seen
significant advancements, given their unique ability to extract relevant features and handle …

Masked Autoencoders learn strong visual representations and achieve state-of-the-art
results in several independent modalities, yet very few works have addressed their …

Abstract Contrastive Language-Image Pre-training (CLIP) has been shown to learn visual
representations with promising zero-shot performance. To further improve its downstream …

In recent years, vision-centric Bird's Eye View (BEV) perception has garnered significant
interest from both industry and academia due to its inherent advantages, such as providing …

Recently, video moment retrieval and highlight detection (MR/HD) are being spotlighted as
the demand for video understanding is drastically increased. The key objective of MR/HD is …

Challenging illumination conditions (low-light, under-exposure and over-exposure) in the
real world not only cast an unpleasant visual appearance but also taint the computer vision …