Open-Vocabulary Segmentation with Unpaired Mask-Text Supervision

Contemporary cutting-edge open-vocabulary segmentation approaches commonly rely on image-mask-text triplets, yet this restricted annotation is labour-intensive and encounters scalability hurdles in complex real-world scenarios. Although some methods are proposed to reduce the annotation cost with only text supervision, the incompleteness of supervision severely limits the versatility and performance. In this paper, we liberate the strict correspondence between masks and texts by using independent image-mask and image-text pairs, which can be easily collected respectively. With this unpaired mask-text supervision, we propose a new weakly-supervised open-vocabulary segmentation framework (Uni-OVSeg) that leverages confident pairs of mask predictions and entities in text descriptions. Using the independent image-mask and image-text pairs, we predict a set of binary masks and associate them with entities by resorting to the CLIP embedding space. However, the inherent noise in the correspondence between masks and entities poses a significant challenge when obtaining reliable pairs. In light of this, we advocate using the large vision-language model (LVLM) to refine text descriptions and devise a multi-scale ensemble to stablise the matching between masks and entities. Compared to text-only weakly-supervised methods, our Uni-OVSeg achieves substantial improvements of 15.5% mIoU on the ADE20K datasets, and even surpasses fully-supervised methods on the challenging PASCAL Context-459 dataset.

An Efficient Transformer for Simultaneous Learning of BEV and Lane Representations in 3D Lane Detection

Accurately detecting lane lines in 3D space is crucial for autonomous driving. Existing methods usually first transform image-view features into bird-eye-view (BEV) by aid of inverse perspective mapping (IPM), and then detect lane lines based on the BEV features. However, IPM ignores the changes in road height, leading to inaccurate view transformations. Additionally, the two separate stages of the process can cause cumulative errors and increased complexity. To address these limitations, we propose an efficient transformer for 3D lane detection. Different from the vanilla transformer, our model contains a decomposed cross-attention mechanism to simultaneously learn lane and BEV representations. The mechanism decomposes the cross-attention between image-view and BEV features into the one between image-view and lane features, and the one between lane and BEV features, both of which are supervised with ground-truth lane lines. Our method obtains 2D and 3D lane predictions by applying the lane features to the image-view and BEV features, respectively. This allows for a more accurate view transformation than IPM-based methods, as the view transformation is learned from data with a supervised cross-attention. Additionally, the cross-attention between lane and BEV features enables them to adjust to each other, resulting in more accurate lane detection than the two separate stages. Finally, the decomposed cross-attention is more efficient than the original one. Experimental results on OpenLane and ONCE-3DLanes demonstrate the state-of-the-art performance of our method.