209 citations · 682 across the 67 of their papers we have counts for
71 papers
ReasonCLIP-58M: Visually Grounded Commonsense Reasoning Supervision for CLIP
Sicheng Zhang, Muzammal Naseer, Binzhu Xie +5
CLIP and its variants are widely adopted visual backbones in multimodal systems, but their pretraining remains dominated by descriptive image-text alignment. As downstream applicat…
D3Seg: Dependency-Aware Diffusion for Brain Tumor Segmentation with Missing Modalities
Danish Ali, Ajmal Mian, Naveed Akhtar +1
Accurate brain tumor segmentation using multi-parametric MRI is critical for effective treatment planning. However, in clinical settings, complete acquisition of all MRI sequences…
Attribution-Guided Model Rectification of Unreliable Neural Network Behaviors
Peiyu Yang, Naveed Akhtar, Jiantong Jiang +1
The performance of neural network models deteriorates due to their unreliable behavior on non-robust features of corrupted samples. Owing to their opaque nature, rectifying models…
DRBD-Mamba for Robust and Efficient Brain Tumor Segmentation with Analytical Insights
Danish Ali, Ajmal Mian, Naveed Akhtar +1
Accurate brain tumor segmentation is significant for clinical diagnosis and treatment but remains challenging due to tumor heterogeneity. Mamba-based State Space Models have demons…
CymbaDiff: Structured Spatial Diffusion for Sketch-based 3D Semantic Urban Scene Generation
Li Liang, Bo Miao, Xinyu Wang +3
Outdoor 3D semantic scene generation produces realistic and semantically rich environments for applications such as urban simulation and autonomous driving. However, advances in th…
On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations
Jordan Vice, Naveed Akhtar, Yansong Gao +2
Vision-Language Models (VLMs) are increasingly used as perceptual modules for visual content reasoning, including through captioning and DeepFake detection. In this work, we expose…
Multistream Network for LiDAR and Camera-based 3D Object Detection in Outdoor Scenes
Muhammad Ibrahim, Naveed Akhtar, Haitian Wang +2
Fusion of LiDAR and RGB data has the potential to enhance outdoor 3D object detection accuracy. To address real-world challenges in outdoor 3D object detection, fusion of LiDAR and…
Deeper Diffusion Models Amplify Bias
Shahin Hakemi, Naveed Akhtar, Ghulam Mubashar Hassan +1
Despite the remarkable performance of generative Diffusion Models (DMs), their internal working is still not well understood, which is potentially problematic. This paper focuses o…
On Transfer-based Universal Attacks in Pure Black-box Setting
Mohammad A. A. K. Jalwana, Naveed Akhtar, Ajmal Mian +2
Despite their impressive performance, deep visual models are susceptible to transferable black-box adversarial attacks. Principally, these attacks craft perturbations in a target m…
Exploring Bias in over 100 Text-to-Image Generative Models
Jordan Vice, Naveed Akhtar, Richard Hartley +1
We investigate bias trends in text-to-image generative models over time, focusing on the increasing availability of models through open platforms like Hugging Face. While these pla…
Image Watermarking of Generative Diffusion Models
Yunzhuo Chen, Jordan Vice, Naveed Akhtar +2
Embedding watermarks into the output of generative models is essential for establishing copyright and verifiable ownership over the generated content. Emerging diffusion model wate…
Deepfake Detection with Spatio-Temporal Consistency and Attention
Yunzhuo Chen, Naveed Akhtar, Nur Al Hasan Haldar +1
Deepfake videos are causing growing concerns among communities due to their ever-increasing realism. Naturally, automated detection of forged Deepfake videos is attracting a propor…
Dynamic watermarks in images generated by diffusion models
Yunzhuo Chen, Naveed Akhtar, Nur Al Hasan Haldar +1
High-fidelity text-to-image diffusion models have revolutionized visual content generation, but their widespread use raises significant ethical concerns, including intellectual pro…
Single-weight Model Editing for Post-hoc Spurious Correlation Neutralization
Shahin Hakemi, Naveed Akhtar, Ghulam Mubashar Hassan +1
Neural network training tends to exploit the simplest features as shortcuts to greedily minimize training loss. However, some of these features might be spuriously correlated with…
Safety Without Semantic Disruptions: Editing-free Safe Image Generation via Context-preserving Dual Latent Reconstruction
Jordan Vice, Naveed Akhtar, Mubarak Shah +2
Training multimodal generative models on large, uncurated datasets can result in users being exposed to harmful, unsafe and controversial or culturally-inappropriate outputs. While…
On the Fairness, Diversity and Reliability of Text-to-Image Generative Models
Jordan Vice, Naveed Akhtar, Leonid Sigal +2
The rapid proliferation of multimodal generative models has sparked critical discussions on their reliability, fairness and potential for misuse. While text-to-image models excel a…
Regulating Model Reliance on Non-Robust Features by Smoothing Input Marginal Density
Peiyu Yang, Naveed Akhtar, Mubarak Shah +1
Trustworthy machine learning necessitates meticulous regulation of model reliance on non-robust features. We propose a framework to delineate and regulate such features by attribut…
A Backdoor-based Explainable AI Benchmark for High Fidelity Evaluation of Attributions
Peiyu Yang, Naveed Akhtar, Jiantong Jiang +1
Attribution methods compute importance scores for input features to explain model predictions. However, assessing the faithfulness of these methods remains challenging due to the a…
Manipulating and Mitigating Generative Model Biases without Retraining
Jordan Vice, Naveed Akhtar, Richard Hartley +1
Text-to-image (T2I) generative models have gained increased popularity in the public domain. While boasting impressive user-guided generative abilities, their black-box nature expo…
SCTransNet: Spatial-channel Cross Transformer Network for Infrared Small Target Detection
Shuai Yuan, Hanlin Qin, Xiang Yan +2
Infrared small target detection (IRSTD) has recently benefitted greatly from U-shaped neural models. However, largely overlooking effective global information modeling, existing te…
ASCNet: Asymmetric Sampling Correction Network for Infrared Image Destriping
Shuai Yuan, Hanlin Qin, Xiang Yan +4
In a real-world infrared imaging system, effectively learning a consistent stripe noise removal model is essential. Most existing destriping methods cannot precisely reconstruct im…
Quantifying Bias in Text-to-Image Generative Models
Jordan Vice, Naveed Akhtar, Richard Hartley +1
Bias in text-to-image (T2I) models can propagate unfair social representations and may be used to aggressively market ideas or push controversial agendas. Existing T2I model bias e…
On quantifying and improving realism of images generated with diffusion
Yunzhuo Chen, Naveed Akhtar, Nur Al Hasan Haldar +1
Recent advances in diffusion models have led to a quantum leap in the quality of generative visual content. However, quantification of realism of the content is still challenging.…
Text-image guided Diffusion Model for generating Deepfake celebrity interactions
Yunzhuo Chen, Nur Al Hasan Haldar, Naveed Akhtar +1
Deepfake images are fast becoming a serious concern due to their realism. Diffusion models have recently demonstrated highly realistic visual content generation, which makes them a…