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Found 6 papers, 5 papers with code
Large Multimodal Agents: A Survey
In this paper, we conduct a systematic review of LLM-driven multimodal agents, which we refer to as large multimodal agents ( LMAs for short).
ALLaVA: Harnessing GPT4V-synthesized Data for A Lite Vision-Language Model
Recent advancements in Large Vision-Language Models (LVLMs) have enabled processing of multimodal inputs in language models but require significant computational resources for deployment, especially in edge devices.
Advancing Visual Grounding with Scene Knowledge: Benchmark and Method
Therefore, in this paper, we propose a novel benchmark of \underline{S}cene \underline{K}nowledge-guided \underline{V}isual \underline{G}rounding (SK-VG), where the image content and referring expressions are not sufficient to ground the target objects, forcing the models to have a reasoning ability on the long-form scene knowledge.
Lesion-aware Dynamic Kernel for Polyp Segmentation
Automatic and accurate polyp segmentation plays an essential role in early colorectal cancer diagnosis.
Self-Supervised Correction Learning for Semi-Supervised Biomedical Image Segmentation
Since the two tasks rely on similar feature information, the unlabeled data effectively enhances the representation of the network to the lesion regions and further improves the segmentation performance.
Adaptive Context Selection for Polyp Segmentation
To tackle these issues, we propose an adaptive context selection based encoder-decoder framework which is composed of Local Context Attention (LCA) module, Global Context Module (GCM) and Adaptive Selection Module (ASM).
