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Found 21 papers, 3 papers with code
UTOPIA: Unconstrained Tracking Objects without Preliminary Examination via Cross-Domain Adaptation
Then, a new cross-domain MOT adaptation from existing datasets is proposed without any pre-defined human knowledge in understanding and modeling objects.
Type-to-Track: Retrieve Any Object via Prompt-based Tracking
This paper introduces a novel paradigm for Multiple Object Tracking called Type-to-Track, which allows users to track objects in videos by typing natural language descriptions.
Grounded Multiple Object Tracking
Multiple Object Tracking
+1
Multi-Camera Multi-Object Tracking on the Move via Single-Stage Global Association Approach
The development of autonomous vehicles generates a tremendous demand for a low-cost solution with a complete set of camera sensors capturing the environment around the car.
Depth Perspective-aware Multiple Object Tracking
This paper aims to tackle Multiple Object Tracking (MOT), an important problem in computer vision but remains challenging due to many practical issues, especially occlusions.
Multi-Camera Multiple 3D Object Tracking on the Move for Autonomous Vehicles
The experimental results on the nuScenes dataset demonstrate the benefits of the proposed method to produce SOTA performance on the existing vision-based tracking dataset.
DyGLIP: A Dynamic Graph Model with Link Prediction for Accurate Multi-Camera Multiple Object Tracking
Multi-Camera Multiple Object Tracking (MC-MOT) is a significant computer vision problem due to its emerging applicability in several real-world applications.
A Multi-task Contextual Atrous Residual Network for Brain Tumor Detection & Segmentation
The first objective is performed by our proposed contextual brain tumor detection network, which plays a role of an attention gate and focuses on the region around brain tumor only while ignoring the far neighbor background which is less correlated to the tumor.
LIAAD: Lightweight Attentive Angular Distillation for Large-scale Age-Invariant Face Recognition
This work presents a novel Lightweight Attentive Angular Distillation (LIAAD) approach to Large-scale Lightweight AiFR that overcomes these limitations.
Vec2Face: Unveil Human Faces from their Blackbox Features in Face Recognition
Unveiling face images of a subject given his/her high-level representations extracted from a blackbox Face Recognition engine is extremely challenging.
ShrinkTeaNet: Million-scale Lightweight Face Recognition via Shrinking Teacher-Student Networks
In addition, this work introduces a novel Angular Distillation Loss for distilling the feature direction and the sample distributions of the teacher's hypersphere to its student.
Non-Volume Preserving-based Fusion to Group-Level Emotion Recognition on Crowd Videos
To demonstrate the robustness and effectiveness of each component in the proposed approach, three experiments were conducted: (i) evaluation on AffectNet database to benchmark the proposed EmoNet for recognizing facial expression; (ii) evaluation on EmotiW2018 to benchmark the proposed deep feature level fusion mechanism NVPF; and, (iii) examine the proposed TNVPF on an innovative Group-level Emotion on Crowd Videos (GECV) dataset composed of 627 videos collected from publicly available sources.
MobiFace: A Lightweight Deep Learning Face Recognition on Mobile Devices
Deep neural networks have been widely used in numerous computer vision applications, particularly in face recognition.
Automatic Face Aging in Videos via Deep Reinforcement Learning
This paper presents a novel approach to synthesize automatically age-progressed facial images in video sequences using Deep Reinforcement Learning.
Longitudinal Face Aging in the Wild - Recent Deep Learning Approaches
Face Aging has raised considerable attentions and interest from the computer vision community in recent years.
Learning from Longitudinal Face Demonstration - Where Tractable Deep Modeling Meets Inverse Reinforcement Learning
The proposed model is experimented in both tasks of face aging synthesis and cross-age face verification.
Reformulating Level Sets as Deep Recurrent Neural Network Approach to Semantic Segmentation
To address these issues and boost the classic variational LS methods to a new level of the learnable deep learning approaches, we propose a novel definition of contour evolution named Recurrent Level Set (RLS)} to employ Gated Recurrent Unit under the energy minimization of a variational LS functional.
Temporal Non-Volume Preserving Approach to Facial Age-Progression and Age-Invariant Face Recognition
Modeling the long-term facial aging process is extremely challenging due to the presence of large and non-linear variations during the face development stages.
Deep Appearance Models: A Deep Boltzmann Machine Approach for Face Modeling
This paper presents a novel Deep Appearance Models (DAMs) approach, an efficient replacement for AAMs, to accurately capture both shape and texture of face images under large variations.
Robust Deep Appearance Models
In this approach, two crucial components of face images, i. e. shape and texture, are represented by Deep Boltzmann Machines and Robust Deep Boltzmann Machines (RDBM), respectively.
Longitudinal Face Modeling via Temporal Deep Restricted Boltzmann Machines
The Temporal Deep Restricted Boltzmann Machines based age progression model together with the prototype faces are then constructed to learn the aging transformation between faces in the sequence.
Beyond Principal Components: Deep Boltzmann Machines for Face Modeling
The "interpretation through synthesis", i. e.
