7 papers · 1 filter
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…
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…
Incremental Open-set Domain Adaptation
Sayan Rakshit, Hmrishav Bandyopadhyay, Nibaran Das +1
Catastrophic forgetting makes neural network models unstable when learning visual domains consecutively. The neural network model drifts to catastrophic forgetting-induced low perf…
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…