40 citations · 206 across the 27 of their papers we have counts for
31 papers
Everybody Tracking Every Body
Daeyun Shin, Yunhan Zhao, Shu Kong +2
We address the problem of 3D body pose estimation of multiple interacting people from their egocentric views with centralized coordination. Each individual wears a camera recording…
Improving Knowledge Distillation via Regularizing Feature Norm and Direction
Yuzhu Wang, Lechao Cheng, Manni Duan +3
Knowledge distillation (KD) exploits a large well-trained model (i.e., teacher) to train a small student model on the same dataset for the same task. Treating teacher features as k…
Far3Det: Towards Far-Field 3D Detection
Shubham Gupta, Jeet Kanjani, Mengtian Li +4
We focus on the task of far-field 3D detection (Far3Det) of objects beyond a certain distance from an observer, e.g., 50m. Far3Det is particularly important for autonomous vehic…
Towards Long-Tailed 3D Detection
Neehar Peri, Achal Dave, Deva Ramanan +1
Contemporary autonomous vehicle (AV) benchmarks have advanced techniques for training 3D detectors, particularly on large-scale lidar data. Surprisingly, although semantic class la…
Continual Learning with Evolving Class Ontologies
Zhiqiu Lin, Deepak Pathak, Yu-Xiong Wang +2
Lifelong learners must recognize concept vocabularies that evolve over time. A common yet underexplored scenario is learning with class labels that continually refine/expand old cl…
Creating a Forensic Database of Shoeprints from Online Shoe Tread Photos
Samia Shafique, Bailey Kong, Shu Kong +1
Shoe tread impressions are one of the most common types of evidence left at crime scenes. However, the utility of such evidence is limited by the lack of databases of footwear prin…
Long-Tailed Recognition via Weight Balancing
Shaden Alshammari, Yu-Xiong Wang, Deva Ramanan +1
In the real open world, data tends to follow long-tailed class distributions, motivating the well-studied long-tailed recognition (LTR) problem. Naive training produces models that…
OpenGAN: Open-Set Recognition via Open Data Generation
Shu Kong, Deva Ramanan
Real-world machine learning systems need to analyze test data that may differ from training data. In K-way classification, this is crisply formulated as open-set recognition, core…
Multimodal Object Detection via Probabilistic Ensembling
Yi-Ting Chen, Jinghao Shi, Zelin Ye +3
Object detection with multimodal inputs can improve many safety-critical systems such as autonomous vehicles (AVs). Motivated by AVs that operate in both day and night, we study mu…
Camera Pose Matters: Improving Depth Prediction by Mitigating Pose Distribution Bias
Yunhan Zhao, Shu Kong, Charless Fowlkes
Monocular depth predictors are typically trained on large-scale training sets which are naturally biased w.r.t the distribution of camera poses. As a result, trained predictors fai…
Weak Supervision and Referring Attention for Temporal-Textual Association Learning
Zhiyuan Fang, Shu Kong, Zhe Wang +2
A system capturing the association between video frames and textual queries offer great potential for better video analysis. However, training such a system in a fully supervised w…
Celeganser: Automated Analysis of Nematode Morphology and Age
Linfeng Wang, Shu Kong, Zachary Pincus +1
The nematode Caenorhabditis elegans (C. elegans) serves as an important model organism in a wide variety of biological studies. In this paper we introduce a pipeline for automated…
Domain Decluttering: Simplifying Images to Mitigate Synthetic-Real Domain Shift and Improve Depth Estimation
Yunhan Zhao, Shu Kong, Daeyun Shin +1
Leveraging synthetically rendered data offers great potential to improve monocular depth estimation and other geometric estimation tasks, but closing the synthetic-real domain gap…
Multigrid Predictive Filter Flow for Unsupervised Learning on Videos
Shu Kong, Charless Fowlkes
We introduce multigrid Predictive Filter Flow (mgPFF), a framework for unsupervised learning on videos. The mgPFF takes as input a pair of frames and outputs per-pixel filters to w…
Modularized Textual Grounding for Counterfactual Resilience
Zhiyuan Fang, Shu Kong, Charless Fowlkes +1
Computer Vision applications often require a textual grounding module with precision, interpretability, and resilience to counterfactual inputs/queries. To achieve high grounding p…
Image Reconstruction with Predictive Filter Flow
Shu Kong, Charless Fowlkes
We propose a simple, interpretable framework for solving a wide range of image reconstruction problems such as denoising and deconvolution. Given a corrupted input image, the model…
Fine-Grained Facial Expression Analysis Using Dimensional Emotion Model
Feng Zhou, Shu Kong, Charless Fowlkes +2
Automated facial expression analysis has a variety of applications in human-computer interaction. Traditional methods mainly analyze prototypical facial expressions of no more than…
Pixel-wise Attentional Gating for Parsimonious Pixel Labeling
Shu Kong, Charless Fowlkes
To achieve parsimonious inference in per-pixel labeling tasks with a limited computational budget, we propose a \emph{Pixel-wise Attentional Gating} unit (\emph{PAG}) that learns t…
Weakly Supervised Attention Learning for Textual Phrases Grounding
Zhiyuan Fang, Shu Kong, Tianshu Yu +1
Grounding textual phrases in visual content is a meaningful yet challenging problem with various potential applications such as image-text inference or text-driven multimedia inter…
Recurrent Pixel Embedding for Instance Grouping
Shu Kong, Charless Fowlkes
We introduce a differentiable, end-to-end trainable framework for solving pixel-level grouping problems such as instance segmentation consisting of two novel components. First, we…
Recurrent Scene Parsing with Perspective Understanding in the Loop
Shu Kong, Charless Fowlkes
Objects may appear at arbitrary scales in perspective images of a scene, posing a challenge for recognition systems that process images at a fixed resolution. We propose a depth-aw…
Low-rank Bilinear Pooling for Fine-Grained Classification
Shu Kong, Charless Fowlkes
Pooling second-order local feature statistics to form a high-dimensional bilinear feature has been shown to achieve state-of-the-art performance on a variety of fine-grained classi…
Photo Aesthetics Ranking Network with Attributes and Content Adaptation
Shu Kong, Xiaohui Shen, Zhe Lin +2
Real-world applications could benefit from the ability to automatically generate a fine-grained ranking of photo aesthetics. However, previous methods for image aesthetics analysis…
Spatially Aware Dictionary Learning and Coding for Fossil Pollen Identification
Shu Kong, Surangi Punyasena, Charless Fowlkes
We propose a robust approach for performing automatic species-level recognition of fossil pollen grains in microscopy images that exploits both global shape and local texture chara…