274 citations · 1.2k across the 31 of their papers we have counts for
36 papers
BaFTA: Backprop-Free Test-Time Adaptation For Zero-Shot Vision-Language Models
Xuefeng Hu, Ke Zhang, Min Sun +3
Large-scale pretrained vision-language models like CLIP have demonstrated remarkable zero-shot image classification capabilities across diverse domains. To enhance CLIP's performan…
CaesarNeRF: Calibrated Semantic Representation for Few-shot Generalizable Neural Rendering
Haidong Zhu, Tianyu Ding, Tianyi Chen +3
Generalizability and few-shot learning are key challenges in Neural Radiance Fields (NeRF), often due to the lack of a holistic understanding in pixel-level rendering. We introduce…
MixNorm: Test-Time Adaptation Through Online Normalization Estimation
Xuefeng Hu, Gokhan Uzunbas, Sirius Chen +4
We present a simple and effective way to estimate the batch-norm statistics during test time, to fast adapt a source model to target test samples. Known as Test-Time Adaptation, mo…
Visual Semantic Role Labeling for Video Understanding
Arka Sadhu, Tanmay Gupta, Mark Yatskar +2
We propose a new framework for understanding and representing related salient events in a video using visual semantic role labeling. We represent videos as a set of related events,…
SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classification
Zijian Hu, Zhengyu Yang, Xuefeng Hu +1
A common classification task situation is where one has a large amount of data available for training, but only a small portion is annotated with class labels. The goal of semi-sup…
SPAN: Spatial Pyramid Attention Network forImage Manipulation Localization
Xuefeng Hu, Zhihan Zhang, Zhenye Jiang +3
We present a novel framework, Spatial Pyramid Attention Network (SPAN) for detection and localization of multiple types of image manipulations. The proposed architecture efficientl…
CPARR: Category-based Proposal Analysis for Referring Relationships
Chuanzi He, Haidong Zhu, Jiyang Gao +2
The task of referring relationships is to localize subject and object entities in an image satisfying a relationship query, which is given in the form of \texttt{<subject, predicat…
Video Object Grounding using Semantic Roles in Language Description
Arka Sadhu, Kan Chen, Ram Nevatia
We explore the task of Video Object Grounding (VOG), which grounds objects in videos referred to in natural language descriptions. Previous methods apply image grounding based algo…
Zero-Shot Grounding of Objects from Natural Language Queries
Arka Sadhu, Kan Chen, Ram Nevatia
A phrase grounding system localizes a particular object in an image referred to by a natural language query. In previous work, the phrases were restricted to have nouns that were e…
Pose-variant 3D Facial Attribute Generation
Feng-Ju Chang, Xiang Yu, Ram Nevatia +1
We address the challenging problem of generating facial attributes using a single image in an unconstrained pose. In contrast to prior works that largely consider generation on 2D…
Activity Driven Weakly Supervised Object Detection
Zhenheng Yang, Dhruv Mahajan, Deepti Ghadiyaram +2
Weakly supervised object detection aims at reducing the amount of supervision required to train detection models. Such models are traditionally learned from images/videos labelled…
PIRC Net : Using Proposal Indexing, Relationships and Context for Phrase Grounding
Rama Kovvuri, Ram Nevatia
Phrase Grounding aims to detect and localize objects in images that are referred to and are queried by natural language phrases. Phrase grounding finds applications in tasks such a…
NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection
JIyang Gao, Jiang Wang, Shengyang Dai +2
The labeling cost of large number of bounding boxes is one of the main challenges for training modern object detectors. To reduce the dependence on expensive bounding box annotatio…
MAC: Mining Activity Concepts for Language-based Temporal Localization
Runzhou Ge, Jiyang Gao, Kan Chen +1
We address the problem of language-based temporal localization in untrimmed videos. Compared to temporal localization with fixed categories, this problem is more challenging as the…
Every Pixel Counts ++: Joint Learning of Geometry and Motion with 3D Holistic Understanding
Chenxu Luo, Zhenheng Yang, Peng Wang +4
Learning to estimate 3D geometry in a single frame and optical flow from consecutive frames by watching unlabeled videos via deep convolutional network has made significant progres…
CTAP: Complementary Temporal Action Proposal Generation
Jiyang Gao, Kan Chen, Ram Nevatia
Temporal action proposal generation is an important task, akin to object proposals, temporal action proposals are intended to capture "clips" or temporal intervals in videos that a…
Every Pixel Counts: Unsupervised Geometry Learning with Holistic 3D Motion Understanding
Zhenheng Yang, Peng Wang, Yang Wang +2
Learning to estimate 3D geometry in a single image by watching unlabeled videos via deep convolutional network has made significant process recently. Current state-of-the-art (SOTA…
Revisiting Temporal Modeling for Video-based Person ReID
Jiyang Gao, Ram Nevatia
Video-based person reID is an important task, which has received much attention in recent years due to the increasing demand in surveillance and camera networks. A typical video-ba…
Motion-Appearance Co-Memory Networks for Video Question Answering
Jiyang Gao, Runzhou Ge, Kan Chen +1
Video Question Answering (QA) is an important task in understanding video temporal structure. We observe that there are three unique attributes of video QA compared with image QA:…
LEGO: Learning Edge with Geometry all at Once by Watching Videos
Zhenheng Yang, Peng Wang, Yang Wang +2
Learning to estimate 3D geometry in a single image by watching unlabeled videos via deep convolutional network is attracting significant attention. In this paper, we introduce a "3…
Knowledge Aided Consistency for Weakly Supervised Phrase Grounding
Kan Chen, Jiyang Gao, Ram Nevatia
Given a natural language query, a phrase grounding system aims to localize mentioned objects in an image. In weakly supervised scenario, mapping between image regions (i.e., propos…
ExpNet: Landmark-Free, Deep, 3D Facial Expressions
Feng-Ju Chang, Anh Tuan Tran, Tal Hassner +3
We describe a deep learning based method for estimating 3D facial expression coefficients. Unlike previous work, our process does not relay on facial landmark detection methods as…
Knowledge Concentration: Learning 100K Object Classifiers in a Single CNN
Jiyang Gao, Zijian, Guo +2
Fine-grained image labels are desirable for many computer vision applications, such as visual search or mobile AI assistant. These applications rely on image classification models…
Unsupervised Learning of Geometry with Edge-aware Depth-Normal Consistency
Zhenheng Yang, Peng Wang, Wei Xu +2
Learning to reconstruct depths in a single image by watching unlabeled videos via deep convolutional network (DCN) is attracting significant attention in recent years. In this pape…