2 citations · 7 across the 17 of their papers we have counts for
13 papers · 1 filter
Test-Time Consistency in Vision Language Models
Shih-Han Chou, Shivam Chandhok, James J. Little +1
Vision-Language Models (VLMs) have achieved impressive performance across a wide range of multimodal tasks, yet they often exhibit inconsistent behavior when faced with semanticall…
Learning What Matters: Prioritized Concept Learning via Relative Error-driven Sample Selection
Shivam Chandhok, Qian Yang, Oscar Manas +3
Instruction tuning has been central to the success of recent vision-language models (VLMs), but it remains expensive-requiring large-scale datasets, high-quality annotations, and l…
MM-R: On (In-)Consistency of Vision-Language Models (VLMs)
Shih-Han Chou, Shivam Chandhok, James J. Little +1
With the advent of LLMs and variants, a flurry of research has emerged, analyzing the performance of such models across an array of tasks. While most studies focus on evaluating th…
SceneGPT: A Language Model for 3D Scene Understanding
Shivam Chandhok
Building models that can understand and reason about 3D scenes is difficult owing to the lack of data sources for 3D supervised training and large-scale training regimes. In this w…
Do Vision-Language Foundational models show Robust Visual Perception?
Shivam Chandhok, Pranav Tandon
Recent advances in vision-language foundational models have enabled development of systems that can perform visual understanding and reasoning tasks. However, it is unclear if thes…
Response Wide Shut: Surprising Observations in Basic Vision Language Model Capabilities
Shivam Chandhok, Wan-Cyuan Fan, Leonid Sigal
Vision-Language Models (VLMs) have emerged as general purpose tools for addressing a variety of complex computer vision problems. Such models have been shown to be highly capable,…
Talk2BEV: Language-enhanced Bird's-eye View Maps for Autonomous Driving
Tushar Choudhary, Vikrant Dewangan, Shivam Chandhok +6
Talk2BEV is a large vision-language model (LVLM) interface for bird's-eye view (BEV) maps in autonomous driving contexts. While existing perception systems for autonomous driving s…
INDIGO: Intrinsic Multimodality for Domain Generalization
Puneet Mangla, Shivam Chandhok, Milan Aggarwal +2
For models to generalize under unseen domains (a.k.a domain generalization), it is crucial to learn feature representations that are domain-agnostic and capture the underlying sema…
Unseen Classes at a Later Time? No Problem
Hari Chandana Kuchibhotla, Sumitra S Malagi, Shivam Chandhok +1
Recent progress towards learning from limited supervision has encouraged efforts towards designing models that can recognize novel classes at test time (generalized zero-shot learn…
Context-Conditional Adaptation for Recognizing Unseen Classes in Unseen Domains
Puneet Mangla, Shivam Chandhok, Vineeth N Balasubramanian +1
Recent progress towards designing models that can generalize to unseen domains (i.e domain generalization) or unseen classes (i.e zero-shot learning) has embarked interest towards…
Learn from Anywhere: Rethinking Generalized Zero-Shot Learning with Limited Supervision
Gaurav Bhatt, Shivam Chandhok, Vineeth N Balasubramanian
A common problem with most zero and few-shot learning approaches is they suffer from bias towards seen classes resulting in sub-optimal performance. Existing efforts aim to utilize…
Structured Latent Embeddings for Recognizing Unseen Classes in Unseen Domains
Shivam Chandhok, Sanath Narayan, Hisham Cholakkal +4
The need to address the scarcity of task-specific annotated data has resulted in concerted efforts in recent years for specific settings such as zero-shot learning (ZSL) and domain…
Two-Level Adversarial Visual-Semantic Coupling for Generalized Zero-shot Learning
Shivam Chandhok, Vineeth N Balasubramanian
The performance of generative zero-shot methods mainly depends on the quality of generated features and how well the model facilitates knowledge transfer between visual and semanti…