2 citations · 3 across the 5 of their papers we have counts for
5 papers
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…
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…
HAVE-Net: Hallucinated Audio-Visual Embeddings for Few-Shot Classification with Unimodal Cues
Ankit Jha, Debabrata Pal, Mainak Singha +2
Recognition of remote sensing (RS) or aerial images is currently of great interest, and advancements in deep learning algorithms added flavor to it in recent years. Occlusion, intr…
GOPro: Generate and Optimize Prompts in CLIP using Self-Supervised Learning
Mainak Singha, Ankit Jha, Biplab Banerjee
Large-scale foundation models, such as CLIP, have demonstrated remarkable success in visual recognition tasks by embedding images in a semantically rich space. Self-supervised lear…
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…