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Found 20 papers, 14 papers with code
Weakly Supervised Video Representation Learning with Unaligned Text for Sequential Videos
Sequential video understanding, as an emerging video understanding task, has driven lots of researchers' attention because of its goal-oriented nature.
Multiple Object Tracking Challenge Technical Report for Team MT_IoT
This is a brief technical report of our proposed method for Multiple-Object Tracking (MOT) Challenge in Complex Environments.
Ranked #2 on
Multi-Object Tracking
on DanceTrack
(using extra training data)
HAM: Hierarchical Attention Model with High Performance for 3D Visual Grounding
To simplify the pipeline, we carefully investigate 3D visual grounding and summarize three fundamental problems about how to develop an end-to-end model with high performance for this task.
A Circular Window-based Cascade Transformer for Online Action Detection
In this paper, we advocate a novel and efficient principle for online action detection.
SVIP: Sequence VerIfication for Procedures in Videos
In this paper, we propose a novel sequence verification task that aims to distinguish positive video pairs performing the same action sequence from negative ones with step-level transformations but still conducting the same task.
Two-stage Visual Cues Enhancement Network for Referring Image Segmentation
Referring Image Segmentation (RIS) aims at segmenting the target object from an image referred by one given natural language expression.
Proxy-bridged Image Reconstruction Network for Anomaly Detection in Medical Images
To mitigate this problem, in this paper, we propose a novel Proxy-bridged Image Reconstruction Network (ProxyAno) for anomaly detection in medical images.
Prior Based Human Completion
One is a structure prior, it uses a human parsing map to represent the human body structure.
Look Before You Leap: Learning Landmark Features for One-Stage Visual Grounding
Then we combine the contextual information from the landmark feature convolution module with the target's visual features for grounding.
Learning to Recommend Frame for Interactive Video Object Segmentation in the Wild
This paper proposes a framework for the interactive video object segmentation (VOS) in the wild where users can choose some frames for annotations iteratively.
Amodal Segmentation Based on Visible Region Segmentation and Shape Prior
In this way, features corresponding to background and occlusion can be suppressed for amodal mask estimation.
SIRI: Spatial Relation Induced Network For Spatial Description Resolution
Spatial Description Resolution, as a language-guided localization task, is proposed for target location in a panoramic street view, given corresponding language descriptions.
Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Images
In the end, we further utilize the reconstructed image to extract the structure and measure the difference between structure extracted from original and the reconstructed image.
Password-conditioned Anonymization and Deanonymization with Face Identity Transformers
Cameras are prevalent in our daily lives, and enable many useful systems built upon computer vision technologies such as smart cameras and home robots for service applications.
PPGNet: Learning Point-Pair Graph for Line Segment Detection
In this paper, we present a novel framework to detect line segments in man-made environments.
Multi-Cell Multi-Task Convolutional Neural Networks for Diabetic Retinopathy Grading
To considering the relationships of images with different stages, we propose a \textbf{Multi-Task} learning strategy which predicts the label with both classification and regression.
Face Aging With Identity-Preserved Conditional Generative Adversarial Networks
By grouping faces with target age together, the objective of face aging is equivalent to transferring aging patterns of faces within the target age group to the face whose aged face is to be synthesized.
Future Frame Prediction for Anomaly Detection â A New Baseline
To predict a future frame with higher quality for normal events, other than the commonly used appearance (spatial) constraints on intensity and gradient, we also introduce a motion (temporal) constraint in video prediction by enforcing the optical flow between predicted frames and ground truth frames to be consistent, and this is the first work that introduces a temporal constraint into the video prediction task.
Future Frame Prediction for Anomaly Detection -- A New Baseline
To predict a future frame with higher quality for normal events, other than the commonly used appearance (spatial) constraints on intensity and gradient, we also introduce a motion (temporal) constraint in video prediction by enforcing the optical flow between predicted frames and ground truth frames to be consistent, and this is the first work that introduces a temporal constraint into the video prediction task.
Ranked #2 on
Traffic Accident Detection
on SA
A Revisit of Sparse Coding Based Anomaly Detection in Stacked RNN Framework
Motivated by the capability of sparse coding based anomaly detection, we propose a Temporally-coherent Sparse Coding (TSC) where we enforce similar neighbouring frames be encoded with similar reconstruction coefficients.
Ranked #20 on
Anomaly Detection
on ShanghaiTech
