2 citations · 5 across the 20 of their papers we have counts for
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Foundation Models and Adaptive Feature Selection: A Synergistic Approach to Video Question Answering
Sai Bhargav Rongali, Mohamad Hassan N C, Ankit Jha +3
This paper tackles the intricate challenge of video question-answering (VideoQA). Despite notable progress, current methods fall short of effectively integrating questions with vid…
GraphVL: Graph-Enhanced Semantic Modeling via Vision-Language Models for Generalized Class Discovery
Bhupendra Solanki, Ashwin Nair, Mainak Singha +3
Generalized Category Discovery (GCD) aims to cluster unlabeled images into known and novel categories using labeled images from known classes. To address the challenge of transferr…
In the Era of Prompt Learning with Vision-Language Models
Ankit Jha
Large-scale foundation models like CLIP have shown strong zero-shot generalization but struggle with domain shifts, limiting their adaptability. In our work, we introduce \textsc{S…
COSMo: CLIP Talks on Open-Set Multi-Target Domain Adaptation
Munish Monga, Sachin Kumar Giroh, Ankit Jha +3
Multi-Target Domain Adaptation (MTDA) entails learning domain-invariant information from a single source domain and applying it to multiple unlabeled target domains. Yet, existing…
Elevating All Zero-Shot Sketch-Based Image Retrieval Through Multimodal Prompt Learning
Mainak Singha, Ankit Jha, Divyam Gupta +2
We address the challenges inherent in sketch-based image retrieval (SBIR) across various settings, including zero-shot SBIR, generalized zero-shot SBIR, and fine-grained zero-shot…
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…