185 citations · 326 across the 41 of their papers we have counts for
28 papers · 1 filter
Universal Pyramid Adversarial Training for Improved ViT Performance
Ping-yeh Chiang, Yipin Zhou, Omid Poursaeed +4
Recently, Pyramid Adversarial training (Herrmann et al., 2022) has been shown to be very effective for improving clean accuracy and distribution-shift robustness of vision transfor…
Language-free Compositional Action Generation via Decoupling Refinement
Xiao Liu, Guangyi Chen, Yansong Tang +3
Composing simple elements into complex concepts is crucial yet challenging, especially for 3D action generation. Existing methods largely rely on extensive neural language annotati…
HNeRV: A Hybrid Neural Representation for Videos
Hao Chen, Matt Gwilliam, Ser-Nam Lim +1
Implicit neural representations store videos as neural networks and have performed well for various vision tasks such as video compression and denoising. With frame index or positi…
VoxelFormer: Bird's-Eye-View Feature Generation based on Dual-view Attention for Multi-view 3D Object Detection
Zhuoling Li, Chuanrui Zhang, Wei-Chiu Ma +5
In recent years, transformer-based detectors have demonstrated remarkable performance in 2D visual perception tasks. However, their performance in multi-view 3D object detection re…
LASER: A Neuro-Symbolic Framework for Learning Spatial-Temporal Scene Graphs with Weak Supervision
Jiani Huang, Ziyang Li, Mayur Naik +1
Supervised approaches for learning spatio-temporal scene graphs (STSG) from video are greatly hindered due to their reliance on STSG-annotated videos, which are labor-intensive to…
Detecting Everything in the Open World: Towards Universal Object Detection
Zhenyu Wang, Yali Li, Xi Chen +4
In this paper, we formally address universal object detection, which aims to detect every scene and predict every category. The dependence on human annotations, the limited visual…
ScribbleSeg: Scribble-based Interactive Image Segmentation
Xi Chen, Yau Shing Jonathan Cheung, Ser-Nam Lim +1
Interactive segmentation enables users to extract masks by providing simple annotations to indicate the target, such as boxes, clicks, or scribbles. Among these interaction formats…
Open-vocabulary Panoptic Segmentation with Embedding Modulation
Xi Chen, Shuang Li, Ser-Nam Lim +2
Open-vocabulary image segmentation is attracting increasing attention due to its critical applications in the real world. Traditional closed-vocabulary segmentation methods are not…
Metric Compatible Training for Online Backfilling in Large-Scale Retrieval
Seonguk Seo, Mustafa Gokhan Uzunbas, Bohyung Han +2
Backfilling is the process of re-extracting all gallery embeddings from upgraded models in image retrieval systems. It inevitably requires a prohibitively large amount of computati…
Open Vocabulary Semantic Segmentation with Patch Aligned Contrastive Learning
Jishnu Mukhoti, Tsung-Yu Lin, Omid Poursaeed +4
We introduce Patch Aligned Contrastive Learning (PACL), a modified compatibility function for CLIP's contrastive loss, intending to train an alignment between the patch tokens of t…
CNeRV: Content-adaptive Neural Representation for Visual Data
Hao Chen, Matt Gwilliam, Bo He +2
Compression and reconstruction of visual data have been widely studied in the computer vision community, even before the popularization of deep learning. More recently, some have u…
: Backward-compatible Training with Basis Transformation
Yifei Zhou, Zilu Li, Abhinav Shrivastava +4
Modern retrieval system often requires recomputing the representation of every piece of data in the gallery when updating to a better representation model. This process is known as…
Diversified Dynamic Routing for Vision Tasks
Botos Csaba, Adel Bibi, Yanwei Li +2
Deep learning models for vision tasks are trained on large datasets under the assumption that there exists a universal representation that can be used to make predictions for all s…
Raising the Bar on the Evaluation of Out-of-Distribution Detection
Jishnu Mukhoti, Tsung-Yu Lin, Bor-Chun Chen +4
In image classification, a lot of development has happened in detecting out-of-distribution (OoD) data. However, most OoD detection methods are evaluated on a standard set of datas…
Three New Validators and a Large-Scale Benchmark Ranking for Unsupervised Domain Adaptation
Kevin Musgrave, Serge Belongie, Ser-Nam Lim
Changes to hyperparameters can have a dramatic effect on model accuracy. Thus, the tuning of hyperparameters plays an important role in optimizing machine-learning models. An integ…
Sample-dependent Adaptive Temperature Scaling for Improved Calibration
Tom Joy, Francesco Pinto, Ser-Nam Lim +2
It is now well known that neural networks can be wrong with high confidence in their predictions, leading to poor calibration. The most common post-hoc approach to compensate for t…
HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions
Yongming Rao, Wenliang Zhao, Yansong Tang +3
Recent progress in vision Transformers exhibits great success in various tasks driven by the new spatial modeling mechanism based on dot-product self-attention. In this paper, we s…
VRAG: Region Attention Graphs for Content-Based Video Retrieval
Kennard Ng, Ser-Nam Lim, Gim Hee Lee
Content-based Video Retrieval (CBVR) is used on media-sharing platforms for applications such as video recommendation and filtering. To manage databases that scale to billions of v…
Visual Prompt Tuning
Menglin Jia, Luming Tang, Bor-Chun Chen +4
The current modus operandi in adapting pre-trained models involves updating all the backbone parameters, ie, full fine-tuning. This paper introduces Visual Prompt Tuning (VPT) as a…
Object-Centric Unsupervised Image Captioning
Zihang Meng, David Yang, Xuefei Cao +2
Image captioning is a longstanding problem in the field of computer vision and natural language processing. To date, researchers have produced impressive state-of-the-art performan…
Unsupervised Domain Adaptation: A Reality Check
Kevin Musgrave, Serge Belongie, Ser-Nam Lim
Interest in unsupervised domain adaptation (UDA) has surged in recent years, resulting in a plethora of new algorithms. However, as is often the case in fast-moving fields, baselin…
AdaViT: Adaptive Vision Transformers for Efficient Image Recognition
Lingchen Meng, Hengduo Li, Bor-Chun Chen +4
Built on top of self-attention mechanisms, vision transformers have demonstrated remarkable performance on a variety of vision tasks recently. While achieving excellent performance…
Differential Motion Evolution for Fine-Grained Motion Deformation in Unsupervised Image Animation
Peirong Liu, Rui Wang, Xuefei Cao +3
Image animation is the task of transferring the motion of a driving video to a given object in a source image. While great progress has recently been made in unsupervised motion tr…
Cross-Modal Retrieval Augmentation for Multi-Modal Classification
Shir Gur, Natalia Neverova, Chris Stauffer +3
Recent advances in using retrieval components over external knowledge sources have shown impressive results for a variety of downstream tasks in natural language processing. Here,…