2 citations · 4 across the 25 of their papers we have counts for
6 papers · 1 filter
C-SAW: Self-Supervised Prompt Learning for Image Generalization in Remote Sensing
Avigyan Bhattacharya, Mainak Singha, Ankit Jha +1
We focus on domain and class generalization problems in analyzing optical remote sensing images, using the large-scale pre-trained vision-language model (VLM), CLIP. While contrast…
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…
AD-CLIP: Adapting Domains in Prompt Space Using CLIP
Mainak Singha, Harsh Pal, Ankit Jha +1
Although deep learning models have shown impressive performance on supervised learning tasks, they often struggle to generalize well when the training (source) and test (target) do…
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…
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…