25 citations · 27 across the 7 of their papers we have counts for
7 papers
3D Multi-View Stylization with Pose-Free Correspondences Matching for Robust 3D Geometry Preservation
Shirsha Bose
Artistic style transfer is well studied for images and videos, but extending it to multi-view 3D scenes remains difficult because stylization can disrupt correspondences needed by…
CDAD-Net: Bridging Domain Gaps in Generalized Category Discovery
Sai Bhargav Rongali, Sarthak Mehrotra, Ankit Jha +5
In Generalized Category Discovery (GCD), we cluster unlabeled samples of known and novel classes, leveraging a training dataset of known classes. A salient challenge arises due to…
Finding Dino: A Plug-and-Play Framework for Zero-Shot Detection of Out-of-Distribution Objects Using Prototypes
Poulami Sinhamahapatra, Franziska Schwaiger, Shirsha Bose +3
Detecting and localising unknown or out-of-distribution (OOD) objects in any scene can be a challenging task in vision, particularly in safety-critical cases involving autonomous s…
Unknown Prompt, the only Lacuna: Unveiling CLIP's Potential for Open Domain Generalization
Mainak Singha, Ankit Jha, Shirsha Bose +3
We delve into Open Domain Generalization (ODG), marked by domain and category shifts between training's labeled source and testing's unlabeled target domains. Existing solutions to…
APPLeNet: Visual Attention Parameterized Prompt Learning for Few-Shot Remote Sensing Image Generalization using CLIP
Mainak Singha, Ankit Jha, Bhupendra Solanki +2
In recent years, the success of large-scale vision-language models (VLMs) such as CLIP has led to their increased usage in various computer vision tasks. These models enable zero-s…
MultiScale Probability Map guided Index Pooling with Attention-based learning for Road and Building Segmentation
Shirsha Bose, Ritesh Sur Chowdhury, Debabrata Pal +3
Efficient road and building footprint extraction from satellite images are predominant in many remote sensing applications. However, precise segmentation map extraction is quite ch…
StyLIP: Multi-Scale Style-Conditioned Prompt Learning for CLIP-based Domain Generalization
Shirsha Bose, Ankit Jha, Enrico Fini +3
Large-scale foundation models, such as CLIP, have demonstrated impressive zero-shot generalization performance on downstream tasks, leveraging well-designed language prompts. Howev…