Bridging Semantic Gaps for Language-Supervised Semantic Segmentation

Vision-Language Pre-training has demonstrated its remarkable zero-shot recognition ability and potential to learn generalizable visual representations from language supervision. Taking a step ahead, language-supervised semantic segmentation enables spatial localization of textual inputs by learning pixel grouping solely from image-text pairs. Nevertheless, the state-of-the-art suffers from clear semantic gaps between visual and textual modality: plenty of visual concepts appeared in images are missing in their paired captions. Such semantic misalignment circulates in pre-training, leading to inferior zero-shot performance in dense predictions due to insufficient visual concepts captured in textual representations. To close such semantic gap, we propose Concept Curation (CoCu), a pipeline that leverages CLIP to compensate for the missing semantics. For each image-text pair, we establish a concept archive that maintains potential visually-matched concepts with our proposed vision-driven expansion and text-to-vision-guided ranking. Relevant concepts can thus be identified via cluster-guided sampling and fed into pre-training, thereby bridging the gap between visual and textual semantics. Extensive experiments over a broad suite of 8 segmentation benchmarks show that CoCu achieves superb zero-shot transfer performance and greatly boosts language-supervised segmentation baseline by a large margin, suggesting the value of bridging semantic gap in pre-training data.

BBS-Net: RGB-D Salient Object Detection with a Bifurcated Backbone Strategy Network

Multi-level feature fusion is a fundamental topic in computer vision for detecting, segmenting, and classifying objects at various scales. When multi-level features meet multi-modal cues, the optimal fusion problem becomes a hot potato. In this paper, we make the first attempt to leverage the inherent multi-modal and multi-level nature of RGB-D salient object detection to develop a novel cascaded refinement network. In particular, we 1) propose a bifurcated backbone strategy (BBS) to split the multi-level features into teacher and student features, and 2) utilize a depth-enhanced module (DEM) to excavate informative parts of depth cues from the channel and spatial views. This fuses RGB and depth modalities in a complementary way. Our simple yet efficient architecture, dubbed Bifurcated Backbone Strategy Network (BBS-Net), is backbone independent, runs in real-time (48 fps), and significantly outperforms 18 SOTAs on seven challenging datasets using four metrics.