Learning multimodal representation is a fundamental task that supports a wide range of applications such as visual-text retrieval. While pioneering approaches e.g., CLIP paves the way by learning separated encoders for different modalities, they struggle to model complex interactions between modalities, resulting in inferior vision and language representation. Recently, researchers have begun to leverage powerful Large Vision-Language Models (LVLMs) for unimodal or multimodal encoding, showing substantial improvement over separated encoder methods. However, we find that directly adapting LVLMs to embedding models suffers from insufficient visual representation and coarse multimodal alignment. To address these issues, we propose a simple yet effective Fine-grained Alignment Matters (FAM) method to achieve fine-grained vision-language embedding learning with LVLMs. First, to close the gap between the pure generation and multimodal embedding using LVLMs, we propose Multi-granularity Aligned Contrastive (MAC) to explicitly learn and align fine-grained modality representations at multiple granularity levels using image-text pairs. Second, to mitigate the insufficiency of visual representation during adapting LVLMs to downstream embedding tasks, we propose a Vision Embedding Inversion Training (VEIN) strategy to encourage the extracted embeddings to preserve fine-grained visual features. Extensive experiments demonstrate the effectiveness of our method, which achieves superior performance on various downstream multimodal datasets.

We study the task of weakly-supervised point cloud semantic segmentation with sparse annotations (e.g., less than 0.1% points are labeled), aiming to reduce the expensive cost of dense annotations. Unfortunately, with extremely sparse annotated points, it is very difficult to extract both contextual and object information for scene understanding such as semantic segmentation. Motivated by masked modeling (e.g., MAE) in image and video representation learning, we seek to endow the power of masked modeling to learn contextual information from sparsely-annotated points. However, directly applying MAE to 3D point clouds with sparse annotations may fail to work. First, it is nontrivial to effectively mask out the informative visual context from 3D point clouds. Second, how to fully exploit the sparse annotations for context modeling remains an open question. In this paper, we propose a simple yet effective Contextual Point Cloud Modeling (CPCM) method that consists of two parts: a region-wise masking (Region-Mask) strategy and a contextual masked training (CMT) method. Specifically, RegionMask masks the point cloud continuously in geometric space to construct a meaningful masked prediction task for subsequent context learning. CMT disentangles the learning of supervised segmentation and unsupervised masked context prediction for effectively learning the very limited labeled points and mass unlabeled points, respectively. Extensive experiments on the widely-tested ScanNet V2 and S3DIS benchmarks demonstrate the superiority of CPCM over the state-of-the-art.

Semantic segmentation requires a large amount of densely annotated data for training and may generalize poorly to novel categories. In real-world applications, we have an urgent need for few-shot semantic segmentation which aims to empower a model to handle unseen object categories with limited data. This task is non-trivial due to several challenges. First, it is difficult to extract the class-relevant information to handle the novel class as only a few samples are available. Second, since the image content can be very complex, the novel class information may be suppressed by the base categories due to limited data. Third, one may easily learn promising base classifiers based on a large amount of training data, but it is non-trivial to exploit the knowledge to train the novel classifiers. More critically, once a novel classifier is built, the output probability space will change. How to maintain the base classifiers and dynamically include the novel classifiers remains an open question. To address the above issues, we propose a Dynamic Extension Network (DENet) in which we dynamically construct and maintain a classifier for the novel class by leveraging the knowledge from the base classes and the information from novel data. More importantly, to overcome the information suppression issue, we design a Guided Attention Module (GAM), which can be plugged into any framework to help learn class-relevant features. Last, rather than directly train the model with limited data, we propose a dynamic extension training algorithm to predict the weights of novel classifiers, which is able to exploit the knowledge of base classifiers by dynamically extending classes during training. The extensive experiments show that our proposed method achieves state-of-the-art performance on the PASCAL-5i and COCO-20i datasets. The source code is available at https://github.com/lizhaoliu-Lec/DENet.