In-context learning (ICL) has emerged as a powerful paradigm for Large Visual Language Models (LVLMs), enabling them to leverage a few examples directly from input contexts. However, the effectiveness of this approach is heavily reliant on the selection of demonstrations, a process that is NP-hard. Traditional strategies, including random, similarity-based sampling and infoscore-based sampling, often lead to inefficiencies or suboptimal performance, struggling to balance both efficiency and effectiveness in demonstration selection. In this paper, we propose a novel demonstration selection framework named Coreset-based Dual Retrieval (CoDR). We show that samples within a diverse subset achieve a higher expected mutual information. To implement this, we introduce a cluster-pruning method to construct a diverse coreset that aligns more effectively with the query while maintaining diversity. Additionally, we develop a dual retrieval mechanism that enhances the selection process by achieving global demonstration selection while preserving efficiency. Experimental results demonstrate that our method significantly improves the ICL performance compared to the existing strategies, providing a robust solution for effective and efficient demonstration selection.

Self-supervised visual pre-training models have achieved significant success without employing expensive annotations. Nevertheless, most of these models focus on iconic single-instance datasets (e.g. ImageNet), ignoring the insufficient discriminative representation for non-iconic multi-instance datasets (e.g. COCO). In this paper, we propose a novel Object Adaptive Dense Pre-training (OADP) method to learn the visual representation directly on the multi-instance datasets (e.g., PASCAL VOC and COCO) for dense prediction tasks (e.g., object detection and instance segmentation). We present a novel object-aware and learning-adaptive random view augmentation to focus the contrastive learning to enhance the discrimination of object presentations from large to small scale during different learning stages. Furthermore, the representations across different scale and resolutions are integrated so that the method can learn diverse representations. In the experiment, we evaluated OADP pre-trained on PASCAL VOC and COCO. Results show that our method has better performances than most existing state-of-the-art methods when transferring to various downstream tasks, including image classification, object detection, instance segmentation and semantic segmentation.

In recent years, sequential action recognition has attracted increasingly attention as it requires long-term sequential and compositional reasoning of human actions and object interactions. Existing methods perform reasoning either by using snippets that cover very short consecutive frames or key frames sampled from segments, which take a bias process of local and global temporal information. We also find ad-hoc training and ensembling of two separate networks using existing sampling strategies can easily outperform complex state-of-the-art methods, which reveals the complementary nature of current sampling strategies. Motivated by this observation, we propose a simple yet efficient strategy named Dense Segmental Sampling (DSS) and a novel network architecture named Temporal Dense Segment Network (TDSN) to capture the complementary information from DSS. Our TDSN achieves excellent results on benchmark action recognition datasets, which not only validate the proposed strategy but also help highlight the importance along this direction for sequential video reasoning.