2 citations · 8 across the 15 of their papers we have counts for
6 papers · 2 filters
Mitigating Performance Discrepancy in Cross-Domain 3D Class-Incremental Learning
Jinge Ma, Gautham Vinod, Bruce Coburn +3
3D perception plays a crucial role in real-world applications such as autonomous driving, robotics, and AR/VR. In practical scenarios, 3D perception models need to continually adap…
Open-KNEAD: Knowledge-grounded Nutrition Estimation via Agentic Decomposition
Bruce Coburn, Jingbo Yue, Jinge Ma +3
Multimodal Large Language Models (MLLMs) are increasingly used for dietary assessment from meal images, where retrieval-augmented grounding was shown to sharpen nutrition estimates…
Not Your Stereo-Typical Estimator: Combining Vision and Language for Volume Perception
Gautham Vinod, Bruce Coburn, Siddeshwar Raghavan +1
Accurate volume estimation of objects from visual data is a long-standing challenge in computer vision with significant applications in robotics, logistics, and smart health. Exist…
DietDelta: A Vision-Language Approach for Dietary Assessment via Before-and-After Images
Gautham Vinod, Siddeshwar Raghavan, Bruce Coburn +1
Accurate dietary assessment is critical for precision nutrition, yet most image-based methods rely on a single pre-consumption image and provide only coarse, meal-level estimates.…
Can You Hear, Localize, and Segment Continually? An Exemplar-Free Continual Learning Benchmark for Audio-Visual Segmentation
Siddeshwar Raghavan, Gautham Vinod, Bruce Coburn +1
Audio-Visual Segmentation (AVS) aims to produce pixel-level masks of sound producing objects in videos, by jointly learning from audio and visual signals. However, real-world envir…
Implicit-Scale 3D Reconstruction for Multi-Food Volume Estimation from Monocular Images
Yuhao Chen, Gautham Vinod, Siddeshwar Raghavan +5
We present Implicit-Scale 3D Reconstruction from Monocular Multi-Food Images, a benchmark dataset designed to advance geometry-based food portion estimation in realistic dining sce…