9 papers
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…
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…
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…
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…
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…
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…