1 citations · 2 across the 9 of their papers we have counts for
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CLIP-Guided SAM: Parameter-Efficient Semantic Conditioning for Promptable Segmentation
Shayan Jalilian, Abdul Bais
Promptable foundation models such as the Segment Anything Model (SAM) produce high-quality masks but remain semantically blind, relying on external prompts to specify categories. E…
SwinIFS: Landmark Guided Swin Transformer For Identity Preserving Face Super Resolution
Habiba Kausar, Saeed Anwar, Omar Jamal Hammad +2
Face super-resolution aims to recover high-quality facial images from severely degraded low-resolution inputs, but remains challenging due to the loss of fine structural details an…
C3Net: Context-Contrast Network for Camouflaged Object Detection
Baber Jan, Aiman H. El-Maleh, Abdul Jabbar Siddiqui +2
Camouflaged object detection identifies objects that blend seamlessly with their surroundings through similar colors, textures, and patterns. This task challenges both traditional…
MSRNet: A Multi-Scale Recursive Network for Camouflaged Object Detection
Leena Alghamdi, Muhammad Usman, Hafeez Anwar +2
Camouflaged object detection is an emerging and challenging computer vision task that requires identifying and segmenting objects that blend seamlessly into their environments due…
SPEGNet: Synergistic Perception-Guided Network for Camouflaged Object Detection
Baber Jan, Saeed Anwar, Aiman H. El-Maleh +2
Camouflaged object detection segments objects with intrinsic similarity and edge disruption. Current detection methods rely on accumulated complex components. Each approach adds co…
SAM-PTx: Text-Guided Fine-Tuning of SAM with Parameter-Efficient, Parallel-Text Adapters
Shayan Jalilian, Abdul Bais
The Segment Anything Model (SAM) has demonstrated impressive generalization in prompt-based segmentation. Yet, the potential of semantic text prompts remains underexplored compared…
Segmenting Visuals With Querying Words: Language Anchors For Semi-Supervised Image Segmentation
Numair Nadeem, Saeed Anwar, Muhammad Hamza Asad +1
Vision Language Models (VLMs) provide rich semantic priors but are underexplored in Semi supervised Semantic Segmentation. Recent attempts to integrate VLMs to inject high level se…
MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking
Numair Nadeem, Muhammad Hamza Asad, Saeed Anwar +1
Semantic segmentation of crops and weeds is crucial for site-specific farm management; however, most existing methods depend on labor intensive pixel-level annotations. A further c…
Bird Eye-View to Street-View: A Survey
Khawlah Bajbaa, Muhammad Usman, Saeed Anwar +2
In recent years, street view imagery has grown to become one of the most important sources of geospatial data collection and urban analytics, which facilitates generating meaningfu…
Improved Crop and Weed Detection with Diverse Data Ensemble Learning
Muhammad Hamza Asad, Saeed Anwar, Abdul Bais
Modern agriculture heavily relies on Site-Specific Farm Management practices, necessitating accurate detection, localization, and quantification of crops and weeds in the field, wh…