Spatio-temporal video grounding aims to localize the spatio-temporal tube in a video
according to the given language query. To eliminate the annotation costs, we make a first …

Abstract Multimodal Visual Object Tracking (VOT) has recently gained significant attention
due to its robustness. Early research focused on fully fine-tuning RGB-based trackers which …

Existing works in few-shot action recognition mostly fine-tune a pre-trained image model and
design sophisticated temporal alignment modules at feature level. However, simply fully fine …

Current methods for few-shot action recognition mainly fall into the metric learning
framework following ProtoNet, which demonstrates the importance of prototypes. Although …

In this paper, we propose a novel Temporal Sequence-Aware Model (TSAM) for few-shot
action recognition (FSAR), which incorporates a sequential perceiver adapter into the pre …

Recently, the rise of large-scale vision-language pretrained models like CLIP, coupled with
the technology of Parameter-Efficient FineTuning (PEFT), has captured substantial attraction …

The integration with CLIP (Contrastive Vision-Language Pre-training) has significantly
refreshed the accuracy leaderboard of FSAR (Few-Shot Action Recognition). However, the …

Recently, the rise of large-scale vision-language pretrained models like CLIP, coupled with
the technology of Parameter-Efficient FineTuning (PEFT), has captured substantial attraction …

The goal of few-shot action recognition is to recognize actions in video sequences for which
there exists only a few training samples. The challenge is to adapt a base model effectively …