Search Results for author:
Found 29 papers, 12 papers with code
ReferEverything: Towards Segmenting Everything We Can Speak of in Videos
We present REM, a framework for segmenting a wide range of concepts in video that can be described through natural language.
GRIN: Zero-Shot Metric Depth with Pixel-Level Diffusion
3D reconstruction from a single image is a long-standing problem in computer vision.
Ranked #5 on
Monocular Depth Estimation
on NYU-Depth V2
(using extra training data)
Dreamitate: Real-World Visuomotor Policy Learning via Video Generation
A key challenge in manipulation is learning a policy that can robustly generalize to diverse visual environments.
Generative Camera Dolly: Extreme Monocular Dynamic Novel View Synthesis
Accurate reconstruction of complex dynamic scenes from just a single viewpoint continues to be a challenging task in computer vision.
pix2gestalt: Amodal Segmentation by Synthesizing Wholes
We introduce pix2gestalt, a framework for zero-shot amodal segmentation, which learns to estimate the shape and appearance of whole objects that are only partially visible behind occlusions.
Understanding Video Transformers via Universal Concept Discovery
Concretely, we seek to explain the decision-making process of video transformers based on high-level, spatiotemporal concepts that are automatically discovered.
Zero-Shot Open-Vocabulary Tracking with Large Pre-Trained Models
This begs the question: can we re-purpose these large-scale pre-trained static image models for open-vocabulary video tracking?
Tracking through Containers and Occluders in the Wild
Tracking objects with persistence in cluttered and dynamic environments remains a difficult challenge for computer vision systems.
Zero-1-to-3: Zero-shot One Image to 3D Object
We introduce Zero-1-to-3, a framework for changing the camera viewpoint of an object given just a single RGB image.
Standing Between Past and Future: Spatio-Temporal Modeling for Multi-Camera 3D Multi-Object Tracking
It emphasizes spatio-temporal continuity and integrates both past and future reasoning for tracked objects.
Breaking the "Object" in Video Object Segmentation
Yet, this important phenomenon is largely absent from existing video object segmentation (VOS) benchmarks.
Object Permanence Emerges in a Random Walk along Memory
This paper proposes a self-supervised objective for learning representations that localize objects under occlusion - a property known as object permanence.
Discovering Objects that Can Move
Our experiments demonstrate that, despite only capturing a small subset of the objects that move, this signal is enough to generalize to segment both moving and static instances of dynamic objects.
Heterogeneous-Agent Trajectory Forecasting Incorporating Class Uncertainty
Reasoning about the future behavior of other agents is critical to safe robot navigation.
Learning to Track with Object Permanence
In this work, we introduce an end-to-end trainable approach for joint object detection and tracking that is capable of such reasoning.
Unsupervised Learning of Video Representations via Dense Trajectory Clustering
This paper addresses the task of unsupervised learning of representations for action recognition in videos.
TAO: A Large-Scale Benchmark for Tracking Any Object
To this end, we ask annotators to label objects that move at any point in the video, and give names to them post factum.
Unlocking the Full Potential of Small Data with Diverse Supervision
Indeed, even the majority of few-shot learning methods rely on a large set of "base classes" for pretraining.
Learning to Track Any Object
Moreover, at test time the same network can be applied to detection and tracking, resulting in a unified approach for the two tasks.
A Study on Action Detection in the Wild
In this work we study the problem of action detection in a highly-imbalanced dataset.
Towards Segmenting Anything That Moves
To address this concern, we propose two new benchmarks for generic, moving object detection, and show that our model matches top-down methods on common categories, while significantly out-performing both top-down and bottom-up methods on never-before-seen categories.
Learning Compositional Representations for Few-Shot Recognition
One of the key limitations of modern deep learning approaches lies in the amount of data required to train them.
A Structured Model For Action Detection
A dominant paradigm for learning-based approaches in computer vision is training generic models, such as ResNet for image recognition, or I3D for video understanding, on large datasets and allowing them to discover the optimal representation for the problem at hand.
Learning to Segment Moving Objects
We formulate this as a learning problem and design our framework with three cues: (i) independent object motion between a pair of frames, which complements object recognition, (ii) object appearance, which helps to correct errors in motion estimation, and (iii) temporal consistency, which imposes additional constraints on the segmentation.
Learning Video Object Segmentation with Visual Memory
The module to build a "visual memory" in video, i. e., a joint representation of all the video frames, is realized with a convolutional recurrent unit learned from a small number of training video sequences.
Learning Motion Patterns in Videos
The problem of determining whether an object is in motion, irrespective of camera motion, is far from being solved.
Weakly-Supervised Semantic Segmentation using Motion Cues
We also demonstrate that the performance of M-CNN learned with 150 weak video annotations is on par with state-of-the-art weakly-supervised methods trained with thousands of images.
Image Segmentation
Weakly supervised Semantic Segmentation
+1
Relational Linear Programs
A relational linear program (RLP) is a declarative LP template defining the objective and the constraints through the logical concepts of objects, relations, and quantified variables.
