12 accepted papers
While Large Vision Language Models (LVLMs) are increasingly deployed in real-world applications, their ability to interpret abstract visual inputs remains limited. Specifically, they struggle to comprehend hand-drawn sketches, a modality that offers an intuitive means of expressing concepts that are
Bayesian additive spanning tree (BAST) is an useful tool for interpretable, non-parametric regression on complex constrained domains. It improves upon the performance of Bayesian additive regression trees (BART) by replacing axis-aligned splits through binary tree components by cuts on a spanning tr…
Visual Prompt Tuning (VPT) of pre-trained Vision Transformers (ViTs) has proven highly effective as a parameter-efficient fine-tuning technique for adapting large models to downstream tasks with limited data. Its parameter efficiency makes it particularly suitable for Federated Learning (FL), where …
"Self-supervised learning (SSL) is critical for learning high-quality representations from unlabeled images at scale. Earlier efforts at reducing the compute requirements of SSL have focused on identifying subsets of training data that are sufficient for training. In addition to using a static repre…
Gaussian process is an indispensable tool in clustering functional data, owing to it’s flexibility and inherent uncertainty quantification. However, when the functional data is observed over a large grid (say, of length p), Gaussian process clustering quickly renders itself infeasible, incurring O(p…
Recent work has shown that deep vision models tend to be overly dependent on low-level or "texture" features, leading to poor generalization. Various data augmentation strategies have been proposed to overcome this so-called texture bias in DNNs. We propose a simple, lightweight adversarial augmenta…
The advances in monocular 3D human pose estimation are dominated by supervised techniques that require large-scale 2D/3D pose annotations. Such methods often behave erratically in the absence of any provision to discard unfamiliar out-of-distribution data. To this end, we cast the 3D human pose lear…
Available 3D human pose estimation approaches leverage different forms of strong (2D/3D pose) or weak (multi-view or depth) paired supervision. Barring synthetic or in-studio domains, acquiring such supervision for each new target environment is highly inconvenient. To this end, we cast 3D pose lear…
Camera captured human pose is an outcome of several sources of variation. Performance of supervised 3D pose estimation approaches comes at the cost of dispensing with variations, such as shape and appearance, that may be useful for solving other related tasks. As a result, the learned model not only…
Person re-identification involves retrieving correct matches for a target image (query) from a set of gallery images, while video based re-identification extends this to the case of query and gallery videos. Typical video-based re-id methods ignore the temporal evolution of the intermediate represen…
Text present in images are not merely strings, they provide useful cues about the image. Despite their utility in better image understanding, scene texts are not used in traditional visual question answering (VQA) models. In this work, we present a VQA model which can read scene texts and perform re…
Knowledge distillation deals with the problem of training a smaller model (