Search Results for author:
Found 23 papers, 12 papers with code
Detecting Twenty-thousand Classes using Image-level Supervision
For the first time, we train a detector with all the twenty-one-thousand classes of the ImageNet dataset and show that it generalizes to new datasets without fine-tuning.
Mask2Former for Video Instance Segmentation
We find Mask2Former also achieves state-of-the-art performance on video instance segmentation without modifying the architecture, the loss or even the training pipeline.
Masked-attention Mask Transformer for Universal Image Segmentation
While only the semantics of each task differ, current research focuses on designing specialized architectures for each task.
Ranked #1 on
Panoptic Segmentation
on COCO minival
Ego4D: Around the World in 3,000 Hours of Egocentric Video
We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite.
An End-to-End Transformer Model for 3D Object Detection
We propose 3DETR, an end-to-end Transformer based object detection model for 3D point clouds.
Ranked #6 on
3D Object Detection
on SUN-RGBD val
Anticipative Video Transformer
We propose Anticipative Video Transformer (AVT), an end-to-end attention-based video modeling architecture that attends to the previously observed video in order to anticipate future actions.
Ranked #1 on
Action Anticipation
on EPIC-KITCHENS-100 (test)
(using extra training data)
3D Spatial Recognition without Spatially Labeled 3D
We introduce WyPR, a Weakly-supervised framework for Point cloud Recognition, requiring only scene-level class tags as supervision.
Physical Reasoning Using Dynamics-Aware Models
A common approach to solving physical reasoning tasks is to train a value learner on example tasks.
Ranked #1 on
Visual Reasoning
on PHYRE-1B-Within
Self-Supervised Pretraining of 3D Features on any Point-Cloud
Pretraining on large labeled datasets is a prerequisite to achieve good performance in many computer vision tasks like 2D object recognition, video classification etc.
Forward Prediction for Physical Reasoning
Physical reasoning requires forward prediction: the ability to forecast what will happen next given some initial world state.
Ranked #2 on
Visual Reasoning
on PHYRE-1B-Within
Video Understanding as Machine Translation
With the advent of large-scale multimodal video datasets, especially sequences with audio or transcribed speech, there has been a growing interest in self-supervised learning of video representations.
CATER: A diagnostic dataset for Compositional Actions & TEmporal Reasoning
In this work, we build a video dataset with fully observable and controllable object and scene bias, and which truly requires spatiotemporal understanding in order to be solved.
Are we asking the right questions in MovieQA?
Joint vision and language tasks like visual question answering are fascinating because they explore high-level understanding, but at the same time, can be more prone to language biases.
MetaPix: Few-Shot Video Retargeting
We address the task of unsupervised retargeting of human actions from one video to another.
CATER: A diagnostic dataset for Compositional Actions and TEmporal Reasoning
In this work, we build a video dataset with fully observable and controllable object and scene bias, and which truly requires spatiotemporal understanding in order to be solved.
DistInit: Learning Video Representations Without a Single Labeled Video
In this work, we propose an alternative approach to learning video representations that require no semantically labeled videos and instead leverages the years of effort in collecting and labeling large and clean still-image datasets.
Ranked #62 on
Action Recognition
on HMDB-51
(using extra training data)
Video Action Transformer Network
We introduce the Action Transformer model for recognizing and localizing human actions in video clips.
Ranked #4 on
Action Recognition
on AVA v2.1
A Better Baseline for AVA
We introduce a simple baseline for action localization on the AVA dataset.
Ranked #10 on
Action Recognition
on AVA v2.1
Binge Watching: Scaling Affordance Learning from Sitcoms
In this paper, we tackle the challenge of creating one of the biggest dataset for learning affordances.
Detect-and-Track: Efficient Pose Estimation in Videos
This paper addresses the problem of estimating and tracking human body keypoints in complex, multi-person video.
Ranked #6 on
Keypoint Detection
on COCO test-challenge
Attentional Pooling for Action Recognition
We introduce a simple yet surprisingly powerful model to incorporate attention in action recognition and human object interaction tasks.
Ranked #6 on
Human-Object Interaction Detection
on HICO
ActionVLAD: Learning spatio-temporal aggregation for action classification
In this work, we introduce a new video representation for action classification that aggregates local convolutional features across the entire spatio-temporal extent of the video.
Learning a Predictable and Generative Vector Representation for Objects
The network consists of two components: (a) an autoencoder that ensures the representation is generative; and (b) a convolutional network that ensures the representation is predictable.
