1 citations · 1 across the 9 of their papers we have counts for
14 papers
A Diagnostic Study of World-Space Human Recovery Under High Camera Dynamics
Hao-Jen Chien, Wei-Lun Chao, Yu-Lun Liu
Monocular world-space human recovery methods excel on current benchmarks, but these rarely include fast camera motion. We introduce the K-Pop stress test, broadcast dance videos th…
Autonomous Driving Research Requires a Community-Driven Data Paradigm
Jinsu Yoo, Zanming Huang, Katie Z Luo +5
Autonomous driving has made remarkable progress, with recent AI advances enabling commercial deployments that are reshaping urban mobility. Yet the field remains far from its unive…
Leveraging Latent Visual Reasoning in Silence
Dongyao Zhu, Zhen Wang, Xi Xiao +7
Latent visual reasoning involves visual evidence more directly in multimodal reasoning by inserting continuous latent tokens before textual generation. However, the necessity of th…
SeamCam: Quantifying Seamless Camouflage via Multi-Cue Visual Detectability
Amin Karimi Monsefi, Abolfazl Meyarian, Mridul Khurana +6
Animals are described as effectively camouflaged when they blend seamlessly with their surrounding, yet no standardized quantitative measure of this seamlessness exists. We address…
TaxaAdapter: Vision Taxonomy Models are Key to Fine-grained Image Generation over the Tree of Life
Mridul Khurana, Amin Karimi Monsefi, Justin Lee +9
Accurately generating images across the Tree of Life is difficult: there are over 10M distinct species on Earth, many of which differ only by subtle visual traits. Despite the rema…
On the Feasibility and Opportunity of Autoregressive 3D Object Detection
Zanming Huang, Jinsu Yoo, Sooyoung Jeon +6
LiDAR-based 3D object detectors typically rely on proposal heads with hand-crafted components like anchor assignment and non-maximum suppression (NMS), complicating training and li…
Lessons and Open Questions from a Unified Study of Camera-Trap Species Recognition Over Time
Sooyoung Jeon, Hongjie Tian, Lemeng Wang +7
Camera traps are vital for large-scale biodiversity monitoring, yet accurate automated analysis remains challenging due to diverse deployment environments. While the computer visio…
When the City Teaches the Car: Label-Free 3D Perception from Infrastructure
Zhen Xu, Jinsu Yoo, Cristian Bautista +7
Building robust 3D perception for self-driving still relies heavily on large-scale data collection and manual annotation, yet this paradigm becomes impractical as deployment expand…
Splannequin: Freezing Monocular Mannequin-Challenge Footage with Dual-Detection Splatting
Hao-Jen Chien, Yi-Chuan Huang, Chung-Ho Wu +2
Synthesizing high-fidelity frozen 3D scenes from monocular Mannequin-Challenge (MC) videos is a unique problem distinct from standard dynamic scene reconstruction. Instead of focus…
Continual Unlearning for Text-to-Image Diffusion Models: A Regularization Perspective
Justin Lee, Zheda Mai, Jinsu Yoo +3
Machine unlearning--the ability to remove designated concepts from a pre-trained model--has advanced rapidly, particularly for text-to-image diffusion models. However, existing met…
Preserving Guidance in Cost-Volume Retrieval under Extreme LiDAR Sparsity in Iterative Stereo
Jinsu Yoo, Sooyoung Jeon, Zanming Huang +2
While accurate LiDAR depth has been shown to improve stereo matching, high-end LiDAR remains costly and difficult to deploy at scale, motivating guidance from sparse LiDAR measurem…
TaxaDiffusion: Progressively Trained Diffusion Model for Fine-Grained Species Generation
Amin Karimi Monsefi, Mridul Khurana, Rajiv Ramnath +3
We propose TaxaDiffusion, a taxonomy-informed training framework for diffusion models to generate fine-grained animal images with high morphological and identity accuracy. Unlike s…
KnobGen: Controlling the Sophistication of Artwork in Sketch-Based Diffusion Models
Pouyan Navard, Amin Karimi Monsefi, Mengxi Zhou +3
Recent advances in diffusion models have significantly improved text-to-image (T2I) generation, but they often struggle to balance fine-grained precision with high-level control. M…
Frequency-Guided Masking for Enhanced Vision Self-Supervised Learning
Amin Karimi Monsefi, Mengxi Zhou, Nastaran Karimi Monsefi +3
We present a novel frequency-based Self-Supervised Learning (SSL) approach that significantly enhances its efficacy for pre-training. Prior work in this direction masks out pre-def…