TL;DR
This paper introduces a novel conjunctive multi-attribute verification mechanism to explicitly combat attribute under-representation and significantly outperforms existing methods and establishes a new state-of-the-art on challenging FG-OVD benchmarks, demonstrating a more robust approach to compositional visual reasoning.
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.
The Robust and Fine-grained training framework for CLIP-based vision-language models (RoFLIP) is proposed, enhancing both the robustness and granularity of vision-language alignment and underscore RoFLIP’s compositional reasoning and generalization abilities.
OVIP-SG is presented, a unified framework for instance-preserving semantic mapping, functional scene partitioning, and language-guided small, fine-grained object retrieval that outperforms ConceptGraphs under a unified evaluation protocol on Replica.
KBMR is proposed, the first MLLM-based embedding retriever tailored for KB-VQA, and an MLLM-based semantic discriminator that generates continuous entity-consistency weights is introduced to tackle the challenge of noisy supervision in Wikipedia-scale retrieval.
To disentangle visual evidence at both semantic and spatial levels, ProtoLIP is introduced, a lightweight prototype-mediated evidence layer that organizes reusable visual prototypes into text-derived semantic families and uses coarse-to-fine evidence routing, where semantic families constrain prototype eligibility and...
Industrial search platforms must efficiently retrieve relevant items from billions of candidates while satisfying both query relevance and user preferences. Generative Search (GS) has emerged as a transformative paradigm that reformulates traditional indexing and matching as an autoregressive generation task. However,...
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