TL;DR
FSG-AID is presented, which integrates fine-grained semantic guidance with an attribute-aware iterative decoder and jointly exploits visual and language features to mine attribute semantics, initialize the target query, and iteratively refine the target representation.
A novel Semantic-Spatial Discriminability Enhancement (SSDE) framework for generalized visual grounding is proposed, which aims to enhance the discriminative ability on fine-grained semantics and spatial localization, improving both cross-modal understanding and instance-level grounding.
The Visually-Guided Disambiguation Aggregation Aggregation (VGD-Agg) framework is proposed, a framework based on a dual-branch fast-slow architecture that enhances discriminability via two learnable tokens and achieves state-of-the-art results on the proposed benchmarks.
This work formulates active learning for VG under the realistic setting where only raw images are available without accompanying text, and introduces Referred Region Ambiguity, a new acquisition function that measures whether the model's confidence collapses onto a single region or disperses across multiple candidates.
A novel framework that mines target knowledge using the strong reasoning capacity of Multimodal Large Language Models (MLLMs) and employs it to enhance FSS, which shows promising results and largely surpasses existing methods.
G2D is proposed, a training-free framework that uses a generative VLM to verify CLIP-retrieved candidates against the image and transfers to DCLIP, WaffleCLIP, and CuPL, supporting a practical interface between discriminative proposal and generative visual reasoning.
This work proposes Language-Aligned Dense perception for COD (LAD-COD), a framework that aligns top-down semantic target guidance with bottom-up hierarchical visual features and allows semantic information to guide localization while preserving the fine structural details needed for camouflage segmentation.
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