1 citations · 1 across the 14 of their papers we have counts for
16 papers
After a Decade: Bringing Shadow Removal into the Real World with Agentic Training Data
Shilin Hu, Jingyi Xu, Dimitris Samaras +1
Shadow removal looks nearly solved on established benchmarks, yet remains brittle in the real world. Models have advanced; the paired training data they rely on have barely changed…
Domain-Grounded Candidate Selection for Agentic Image Editing: A Shadow Removal Case
Shilin Hu, Jingyi Xu, Dimitris Samaras +1
Commercial vision-language models are reshaping computer vision, with visual priors broad enough to rival task-specific systems. This raises a natural question: do they reduce the…
Counting Trees from Satellite Imagery with Noisy Supervision
Dimitri Gominski, Maurice Mugabowindekwe, Qiue Xu +6
Counting individual trees is a fundamental task for environmental monitoring, yet remains largely unexplored with satellite imagery. At these resolutions, isolated trees may still…
Personalized Image Descriptions from Attention Sequences
Ruoyu Xue, Hieu Le, Jingyi Xu +5
People can view the same image differently: they focus on different regions, objects, and details in varying orders and describe them in distinct linguistic styles. This leads to s…
Cast and Attached Shadow Detection via Iterative Light and Geometry Reasoning
Shilin Hu, Jingyi Xu, Sagnik Das +2
Shadows encode rich information about scene geometry and illumination, yet existing methods either predict a unified shadow mask or overlook attached shadows entirely. We address t…
Embedding Physical Reasoning into Diffusion-Based Shadow Generation Under the Sun and Sky
Shilin Hu, Jingyi Xu, Akshat Dave +2
Generating realistic cast shadows for inserted foreground objects requires reasoning about scene geometry and illumination. However, most learning-based approaches treat shadow gen…
CORA: Consistency-Guided Semi-Supervised Framework for Reasoning Segmentation
Prantik Howlader, Hoang Nguyen-Canh, Srijan Das +3
Reasoning segmentation seeks pixel-accurate masks for targets referenced by complex, often implicit instructions, requiring context-dependent reasoning over the scene. Recent multi…
Talking Head Generation via AU-Guided Landmark Prediction
Shao-Yu Chang, Jingyi Xu, Hieu Le +1
We propose a two-stage framework for audio-driven talking head generation with fine-grained expression control via facial Action Units (AUs). Unlike prior methods relying on emotio…
What is the Right Embedding Space for Contrastive Learning in Referring Expression Counting?
Kostas Triaridis, Panagiotis Kaliosis, E-Ro Nguyen +3
Referring Expression Counting (REC) requires distinguishing visually similar objects described by fine-grained text cues. Existing methods tackle this via image-text contrastive le…
Few-shot Personalized Scanpath Prediction
Ruoyu Xue, Jingyi Xu, Sounak Mondal +4
A personalized model for scanpath prediction provides insights into the visual preferences and attention patterns of individual subjects. However, existing methods for training sca…
Phrase-Instance Alignment for Generalized Referring Segmentation
E-Ro Nguyen, Hieu Le, Dimitris Samaras +1
Generalized Referring expressions can describe one object, several related objects, or none at all. Existing generalized referring segmentation (GRES) models treat all cases alike,…
Shadow Removal Refinement via Material-Consistent Shadow Edges
Shilin Hu, Hieu Le, ShahRukh Athar +2
Shadow boundaries can be confused with material boundaries as both exhibit sharp changes in luminance or contrast within a scene. However, shadows do not modify the intrinsic color…
Assessing Sample Quality via the Latent Space of Generative Models
Jingyi Xu, Hieu Le, Dimitris Samaras
Advances in generative models increase the need for sample quality assessment. To do so, previous methods rely on a pre-trained feature extractor to embed the generated samples and…
Weighting Pseudo-Labels via High-Activation Feature Index Similarity and Object Detection for Semi-Supervised Segmentation
Prantik Howlader, Hieu Le, Dimitris Samaras
Semi-supervised semantic segmentation methods leverage unlabeled data by pseudo-labeling them. Thus the success of these methods hinges on the reliablility of the pseudo-labels. Ex…
Beyond Pixels: Semi-Supervised Semantic Segmentation with a Multi-scale Patch-based Multi-Label Classifier
Prantik Howlader, Srijan Das, Hieu Le +1
Incorporating pixel contextual information is critical for accurate segmentation. In this paper, we show that an effective way to incorporate contextual information is through a pa…
Learning Relighting and Intrinsic Decomposition in Neural Radiance Fields
Yixiong Yang, Shilin Hu, Haoyu Wu +3
The task of extracting intrinsic components, such as reflectance and shading, from neural radiance fields is of growing interest. However, current methods largely focus on syntheti…