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20152025
most citedLearning Classifiers from Synthetic Data Using a Multichannel Autoencoder

28 citations · 42 across the 13 of their papers we have counts for

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15 papers · 1 filter

cs.CV2025

MaskMed: Decoupled Mask and Class Prediction for Medical Image Segmentation

Bin Xie, Gady Agam

Medical image segmentation typically adopts a point-wise convolutional segmentation head to predict dense labels, where each output channel is heuristically tied to a specific clas…

cs.CV20251 cited

MSLoRA: Multi-Scale Low-Rank Adaptation via Attention Reweighting

Xu Yang, Gady Agam

We introduce MSLoRA, a backbone-agnostic, parameter-efficient adapter that reweights feature responses rather than re-tuning the underlying backbone. Existing low-rank adaptation m…

cs.CV20251 cited

Motion-Aware Transformer for Multi-Object Tracking

Xu Yang, Gady Agam

Multi-object tracking (MOT) in videos remains challenging due to complex object motions and crowded scenes. Recent DETR-based frameworks offer end-to-end solutions but typically pr…

cs.CV20252 cited

RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2

Bin Xie, Hao Tang, Yan Yan +1

Segment Anything Model 2 (SAM 2), a prompt-driven foundation model extending SAM to both image and video domains, has shown superior zero-shot performance compared to its predecess…

cs.CV20251 cited

Self-Prompt SAM: Medical Image Segmentation via Automatic Prompt SAM Adaptation

Bin Xie, Hao Tang, Dawen Cai +2

Segment Anything Model (SAM) has demonstrated impressive zero-shot performance and brought a range of unexplored capabilities to natural image segmentation tasks. However, as a ver…

cs.CV2024

Fine-grained Text to Image Synthesis

Xu Ouyang, Ying Chen, Kaiyue Zhu +1

Fine-grained text to image synthesis involves generating images from texts that belong to different categories. In contrast to general text to image synthesis, in fine-grained synt…