Search Results for author:
Found 27 papers, 9 papers with code
InVi: Object Insertion In Videos Using Off-the-Shelf Diffusion Models
This process begins with inserting the object into a single frame using a ControlNet-based inpainting diffusion model, and then generating subsequent frames conditioned on features from an inpainted frame as an anchor to minimize the domain gap between the background and the object.
GenMM: Geometrically and Temporally Consistent Multimodal Data Generation for Video and LiDAR
Multimodal synthetic data generation is crucial in domains such as autonomous driving, robotics, augmented/virtual reality, and retail.
LD-ZNet: A Latent Diffusion Approach for Text-Based Image Segmentation
First, we show that the latent space of LDMs (z-space) is a better input representation compared to other feature representations like RGB images or CLIP encodings for text-based image segmentation.
Scale Normalized Image Pyramids with AutoFocus for Object Detection
The resulting algorithm is referred to as AutoFocus and results in a 2. 5-5 times speed-up during inference when used with SNIP.
ASAP-NMS: Accelerating Non-Maximum Suppression Using Spatially Aware Priors
The widely adopted sequential variant of Non Maximum Suppression (or Greedy-NMS) is a crucial module for object-detection pipelines.
RSO: A Gradient Free Sampling Based Approach For Training Deep Neural Networks
To this end, RSO adds a perturbation to a weight in a deep neural network and tests if it reduces the loss on a mini-batch.
Recognizing Instagram Filtered Images with Feature De-stylization
Deep neural networks have been shown to suffer from poor generalization when small perturbations are added (like Gaussian noise), yet little work has been done to evaluate their robustness to more natural image transformations like photo filters.
Automatic Long-Term Deception Detection in Group Interaction Videos
Most work on automated deception detection (ADD) in video has two restrictions: (i) it focuses on a video of one person, and (ii) it focuses on a single act of deception in a one or two minute video.
An Analysis of Pre-Training on Object Detection
We analyze how well their features generalize to tasks like image classification, semantic segmentation and object detection on small datasets like PASCAL-VOC, Caltech-256, SUN-397, Flowers-102 etc.
TAN: Temporal Aggregation Network for Dense Multi-label Action Recognition
We present Temporal Aggregation Network (TAN) which decomposes 3D convolutions into spatial and temporal aggregation blocks.
FA-RPN: Floating Region Proposals for Face Detection
We propose a novel approach for generating region proposals for performing face-detection.
AutoFocus: Efficient Multi-Scale Inference
Instead of processing an entire image pyramid, AutoFocus adopts a coarse to fine approach and only processes regions which are likely to contain small objects at finer scales.
Soft Sampling for Robust Object Detection
Interestingly, we observe that after dropping 30% of the annotations (and labeling them as background), the performance of CNN-based object detectors like Faster-RCNN only drops by 5% on the PASCAL VOC dataset.
An Analysis of Scale Invariance in Object Detection  SNIP
On the COCO dataset, our single model performance is 45. 7% and an ensemble of 3 networks obtains an mAP of 48. 3%.
SNIPER: Efficient Multi-Scale Training
Our implementation based on Faster-RCNN with a ResNet-101 backbone obtains an mAP of 47. 6% on the COCO dataset for bounding box detection and can process 5 images per second during inference with a single GPU.
Ranked #126 on
Object Detection
on COCO test-dev
Deception Detection in Videos
We present a system for covert automated deception detection in real-life courtroom trial videos.
R-FCN-3000 at 30fps: Decoupling Detection and Classification
Our approach is a modification of the R-FCN architecture in which position-sensitive filters are shared across different object classes for performing localization.
An Analysis of Scale Invariance in Object Detection - SNIP
On the COCO dataset, our single model performance is 45. 7% and an ensemble of 3 networks obtains an mAP of 48. 3%.
Ranked #134 on
Object Detection
on COCO test-dev
Temporal Context Network for Activity Localization in Videos
For each temporal segment inside a proposal, features are uniformly sampled at a pair of scales and are input to a temporal convolutional neural network for classification.
Ranked #7 on
Action Recognition
on THUMOS’14
Soft-NMS -- Improving Object Detection With One Line of Code
To this end, we propose Soft-NMS, an algorithm which decays the detection scores of all other objects as a continuous function of their overlap with M. Hence, no object is eliminated in this process.
Fast-AT: Fast Automatic Thumbnail Generation using Deep Neural Networks
It is a fully-convolutional deep neural network, which learns specific filters for thumbnails of different sizes and aspect ratios.
A Multi-Stream Bi-Directional Recurrent Neural Network for Fine-Grained Action Detection
We present a multi-stream bi-directional recurrent neural network for fine-grained action detection.
Action Recognition In Videos
Fine-Grained Action Detection
+2
Training Neural Networks Without Gradients: A Scalable ADMM Approach
With the growing importance of large network models and enormous training datasets, GPUs have become increasingly necessary to train neural networks.
VRFP: On-the-fly Video Retrieval using Web Images and Fast Fisher Vector Products
VRFP is a real-time video retrieval framework based on short text input queries, which obtains weakly labeled training images from the web after the query is known.
Layer-Specific Adaptive Learning Rates for Deep Networks
In this paper, we attempt to overcome the two above problems by proposing an optimization method for training deep neural networks which uses learning rates which are both specific to each layer in the network and adaptive to the curvature of the function, increasing the learning rate at low curvature points.
Selecting Relevant Web Trained Concepts for Automated Event Retrieval
Given a text description of an event, event retrieval is performed by selecting concepts linguistically related to the event description and fusing the concept responses on unseen videos.
