Search Results for author:
Found 17 papers, 8 papers with code
StereoVoxelNet: Real-Time Obstacle Detection Based on Occupancy Voxels from a Stereo Camera Using Deep Neural Networks
Obstacle detection is a safety-critical problem in robot navigation, where stereo matching is a popular vision-based approach.
PlanarRecon: Real-time 3D Plane Detection and Reconstruction from Posed Monocular Videos
We present PlanarRecon -- a novel framework for globally coherent detection and reconstruction of 3D planes from a posed monocular video.
Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions
A significant gap remains between today's visual pattern recognition models and human-level visual cognition especially when it comes to few-shot learning and compositional reasoning of novel concepts.
Ranked #1 on
Few-Shot Image Classification
on Bongard-HOI
Few-Shot Image Classification
Human-Object Interaction Detection
+3
RelViT: Concept-guided Vision Transformer for Visual Relational Reasoning
This task remains challenging for current deep learning algorithms since it requires addressing three key technical problems jointly: 1) identifying object entities and their properties, 2) inferring semantic relations between pairs of entities, and 3) generalizing to novel object-relation combinations, i. e., systematic generalization.
Ranked #1 on
Zero-Shot Human-Object Interaction Detection
on HICO
DCVNet: Dilated Cost Volume Networks for Fast Optical Flow
To address this, a sequential strategy is usually adopted, where correspondence sampling in a local neighborhood with a small radius suffices.
In Defense of Grid Features for Visual Question Answering
Popularized as 'bottom-up' attention, bounding box (or region) based visual features have recently surpassed vanilla grid-based convolutional features as the de facto standard for vision and language tasks like visual question answering (VQA).
Ranked #20 on
Visual Question Answering
on VQA v2 test-dev
SENSE: a Shared Encoder Network for Scene-flow Estimation
We introduce a compact network for holistic scene flow estimation, called SENSE, which shares common encoder features among four closely-related tasks: optical flow estimation, disparity estimation from stereo, occlusion estimation, and semantic segmentation.
Automatic adaptation of object detectors to new domains using self-training
Our results demonstrate the usefulness of incorporating hard examples obtained from tracking, the advantage of using soft-labels via distillation loss versus hard-labels, and show promising performance as a simple method for unsupervised domain adaptation of object detectors, with minimal dependence on hyper-parameters.
Unsupervised Hard Example Mining from Videos for Improved Object Detection
In this work, we show how large numbers of hard negatives can be obtained {\em automatically} by analyzing the output of a trained detector on video sequences.
Self-Supervised Relative Depth Learning for Urban Scene Understanding
As an agent moves through the world, the apparent motion of scene elements is (usually) inversely proportional to their depth.
Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation
Finally, the two input images are warped and linearly fused to form each intermediate frame.
Reasoning about Fine-grained Attribute Phrases using Reference Games
We collect a large dataset of such phrases by asking annotators to describe several visual differences between a pair of instances within a category.
Face Detection with the Faster R-CNN
The Faster R-CNN has recently demonstrated impressive results on various object detection benchmarks.
Weakly Supervised Learning for Salient Object Detection
Given a set of background images and salient object images, we propose a solution toward jointly addressing the salient object existence and detection tasks.
Salient Object Detection: A Benchmark
We extensively compare, qualitatively and quantitatively, 40 state-of-the-art models (28 salient object detection, 10 fixation prediction, 1 objectness, and 1 baseline) over 6 challenging datasets for the purpose of benchmarking salient object detection and segmentation methods.
Salient Object Detection: A Survey
Detecting and segmenting salient objects from natural scenes, often referred to as salient object detection, has attracted great interest in computer vision.
Salient Object Detection: A Discriminative Regional Feature Integration Approach
Our method, which is based on multi-level image segmentation, utilizes the supervised learning approach to map the regional feature vector to a saliency score.
