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Found 7 papers, 4 papers with code
MediSee: Reasoning-based Pixel-level Perception in Medical Images
In this paper, we introduce a novel medical vision task: Medical Reasoning Segmentation and Detection (MedSD), which aims to comprehend implicit queries about medical images and generate the corresponding segmentation mask and bounding box for the target object.
Improving Skeleton-based Action Recognition with Interactive Object Information
We propose a new action recognition framework introducing object nodes to supplement absent interactive object information.
Envisioning Class Entity Reasoning by Large Language Models for Few-shot Learning
Few-shot learning (FSL) aims to recognize new concepts using a limited number of visual samples.
CM-UNet: Hybrid CNN-Mamba UNet for Remote Sensing Image Semantic Segmentation
In this paper, we propose CM-UNet, comprising a CNN-based encoder for extracting local image features and a Mamba-based decoder for aggregating and integrating global information, facilitating efficient semantic segmentation of remote sensing images.
SYNC-CLIP: Synthetic Data Make CLIP Generalize Better in Data-Limited Scenarios
Prompt learning is a powerful technique for transferring Vision-Language Models (VLMs) such as CLIP to downstream tasks.
Prompt-based test-time real image dehazing: a novel pipeline
Accordingly, we first apply a prompt generation module (PGM) to generate a visual prompt, which is the reference of appropriate statistical perturbations for mean and standard deviation.
Accurate and lightweight dehazing via multi-receptive-field non-local network and novel contrastive regularization
Thus, a multi-receptive-field non-local network (MRFNLN) consisting of the multi-stream feature attention block (MSFAB) and cross non-local block (CNLB) is presented in this paper.
