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20232026
most citedTarget-driven Attack for Large Language Models

3 citations · 7 across the 8 of their papers we have counts for

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6 papers · 1 filter

cs.CV2026

StealthMark: Harmless and Stealthy Ownership Verification for Medical Segmentation via Uncertainty-Guided Backdoors

Qinkai Yu, Chong Zhang, Gaojie Jin +11

Annotating medical data for training AI models is often costly and limited due to the shortage of specialists with relevant clinical expertise. This challenge is further compounded…

cs.CV2025

Performance is not All You Need: Sustainability Considerations for Algorithms

Xiang Li, Chong Zhang, Hongpeng Wang +3

This work focuses on the high carbon emissions generated by deep learning model training, specifically addressing the core challenge of balancing algorithm performance and energy c…

cs.CV20242 cited

Better Sampling, towards Better End-to-end Small Object Detection

Zile Huang, Chong Zhang, Mingyu Jin +3

While deep learning-based general object detection has made significant strides in recent years, the effectiveness and efficiency of small object detection remain unsatisfactory. T…

cs.CV2023

Bridging the Projection Gap: Overcoming Projection Bias Through Parameterized Distance Learning

Chong Zhang, Mingyu Jin, Qinkai Yu +3

Generalized zero-shot learning (GZSL) aims to recognize samples from both seen and unseen classes using only seen class samples for training. However, GZSL methods are prone to bia…

cs.CV2023

A Simple and Effective Baseline for Attentional Generative Adversarial Networks

Mingyu Jin, Chong Zhang, Qinkai Yu +3

Synthesising a text-to-image model of high-quality images by guiding the generative model through the Text description is an innovative and challenging task. In recent years, AttnG…

cs.CV2023

Image Blending Algorithm with Automatic Mask Generation

Haochen Xue, Mingyu Jin, Chong Zhang +3

In recent years, image blending has gained popularity for its ability to create visually stunning content. However, the current image blending algorithms mainly have the following…