306 citations · 406 across the 10 of their papers we have counts for
11 papers
Test-Time Training on Video Streams
Renhao Wang, Yu Sun, Arnuv Tandon +4
Prior work has established Test-Time Training (TTT) as a general framework to further improve a trained model at test time. Before making a prediction on each test instance, the mo…
Improving Selective Visual Question Answering by Learning from Your Peers
Corentin Dancette, Spencer Whitehead, Rishabh Maheshwary +5
Despite advances in Visual Question Answering (VQA), the ability of models to assess their own correctness remains underexplored. Recent work has shown that VQA models, out-of-the-…
R-MAE: Regions Meet Masked Autoencoders
Duy-Kien Nguyen, Vaibhav Aggarwal, Yanghao Li +4
In this work, we explore regions as a potential visual analogue of words for self-supervised image representation learning. Inspired by Masked Autoencoding (MAE), a generative pre-…
EurNet: Efficient Multi-Range Relational Modeling of Spatial Multi-Relational Data
Minghao Xu, Yuanfan Guo, Yi Xu +3
Modeling spatial relationship in the data remains critical across many different tasks, such as image classification, semantic segmentation and protein structure understanding. Pre…
LoopITR: Combining Dual and Cross Encoder Architectures for Image-Text Retrieval
Jie Lei, Xinlei Chen, Ning Zhang +4
Dual encoders and cross encoders have been widely used for image-text retrieval. Between the two, the dual encoder encodes the image and text independently followed by a dot produc…
Benchmarking Detection Transfer Learning with Vision Transformers
Yanghao Li, Saining Xie, Xinlei Chen +3
Object detection is a central downstream task used to test if pre-trained network parameters confer benefits, such as improved accuracy or training speed. The complexity of object…
Towards Demystifying Representation Learning with Non-contrastive Self-supervision
Xiang Wang, Xinlei Chen, Simon S. Du +1
Non-contrastive methods of self-supervised learning (such as BYOL and SimSiam) learn representations by minimizing the distance between two views of the same image. These approache…
KRISP: Integrating Implicit and Symbolic Knowledge for Open-Domain Knowledge-Based VQA
Kenneth Marino, Xinlei Chen, Devi Parikh +2
One of the most challenging question types in VQA is when answering the question requires outside knowledge not present in the image. In this work we study open-domain knowledge, t…
Overcoming Statistical Shortcuts for Open-ended Visual Counting
Corentin Dancette, Remi Cadene, Xinlei Chen +1
Machine learning models tend to over-rely on statistical shortcuts. These spurious correlations between parts of the input and the output labels does not hold in real-world setting…
ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image Votes
Charles R. Qi, Xinlei Chen, Or Litany +1
3D object detection has seen quick progress thanks to advances in deep learning on point clouds. A few recent works have even shown state-of-the-art performance with just point clo…
nocaps: novel object captioning at scale
Harsh Agrawal, Karan Desai, Yufei Wang +7
Image captioning models have achieved impressive results on datasets containing limited visual concepts and large amounts of paired image-caption training data. However, if these m…