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Found 25 papers, 4 papers with code
Multi-Person 3D Pose Estimation from Multi-View Uncalibrated Depth Cameras
In order to evaluate our proposed pipeline, we collect three video sets of RGBD videos recorded from multiple sparse-view depth cameras and ground truth 3D poses are manually annotated.
Flexible Depth Completion for Sparse and Varying Point Densities
While recent depth completion methods have achieved remarkable results filling in relatively dense depth maps (e. g. projected 64-line LiDAR on KITTI or 500 sampled points on NYUv2) with RGB guidance their performance on very sparse input (e. g. 4-line LiDAR or 32 depth point measurements) is unverified.
3D-CLFusion: Fast Text-to-3D Rendering with Contrastive Latent Diffusion
We note that the novelty of our model lies in that we introduce contrastive learning during training the diffusion prior which enables the generation of the valid view-invariant latent code.
Azimuth Super-Resolution for FMCW Radar in Autonomous Driving
We also propose a hybrid super-resolution model (Hybrid-SR) combining our ADC-SR with a standard RAD super-resolution model, and show that performance can be improved by a large margin.
3D-Aware Encoding for Style-based Neural Radiance Fields
In the first stage, we introduce a base encoder that converts the input image to a latent code.
Domain Adaptive Hand Keypoint and Pixel Localization in the Wild
We aim to improve the performance of regressing hand keypoints and segmenting pixel-level hand masks under new imaging conditions (e. g., outdoors) when we only have labeled images taken under very different conditions (e. g., indoors).
Modality-Agnostic Learning for Radar-Lidar Fusion in Vehicle Detection
To mitigate this problem, we propose the Self-Training Multimodal Vehicle Detection Network (ST-MVDNet) which leverages a Teacher-Student mutual learning framework and a simulated sensor noise model used in strong data augmentation for Lidar and Radar.
Cross-Domain Adaptive Teacher for Object Detection
To mitigate this problem, we propose a teacher-student framework named Adaptive Teacher (AT) which leverages domain adversarial learning and weak-strong data augmentation to address the domain gap.
Adaptive Unbiased Teacher for Cross-Domain Object Detection
This enables the student model to capture domain-invariant features.
Wide-Baseline Multi-Camera Calibration using Person Re-Identification
We address the problem of estimating the 3D pose of a network of cameras for large-environment wide-baseline scenarios, e. g., cameras for construction sites, sports stadiums, and public spaces.
Visio-Temporal Attention for Multi-Camera Multi-Target Association
We propose a inter-tracklet (person to person) attention mechanism that learns a representation for a target tracklet while taking into account other tracklets across multiple views.
Semantics-Guided Representation Learning with Applications to Visual Synthesis
Learning interpretable and interpolatable latent representations has been an emerging research direction, allowing researchers to understand and utilize the derived latent space for further applications such as visual synthesis or recognition.
Semantics-Guided Clustering with Deep Progressive Learning for Semi-Supervised Person Re-identification
As a result, our approach is able to augment the labeled training data in the semi-supervised setting.
Transforming Multi-Concept Attention into Video Summarization
Video summarization is among challenging tasks in computer vision, which aims at identifying highlight frames or shots over a lengthy video input.
Learning Shape Representations for Clothing Variations in Person Re-Identification
To tackle the re-ID problem in the context of clothing changes, we propose a novel representation learning model which is able to generate a body shape feature representation without being affected by clothing color or patterns.
Cross-Resolution Adversarial Dual Network for Person Re-Identification and Beyond
Person re-identification (re-ID) aims at matching images of the same person across camera views.
Cross-Dataset Person Re-Identification via Unsupervised Pose Disentanglement and Adaptation
Person re-identification (re-ID) aims at recognizing the same person from images taken across different cameras.
Ranked #17 on
Unsupervised Domain Adaptation
on Market to Duke
Recover and Identify: A Generative Dual Model for Cross-Resolution Person Re-Identification
Person re-identification (re-ID) aims at matching images of the same identity across camera views.
Learning Resolution-Invariant Deep Representations for Person Re-Identification
Moreover, the extension of our model for semi-supervised re-ID further confirms the scalability of our proposed method for real-world scenarios and applications.
Dual-modality seq2seq network for audio-visual event localization
Audio-visual event localization requires one to identify theevent which is both visible and audible in a video (eitherat a frame or video level).
Deep Reinforcement Learning for Playing 2.5D Fighting Games
Deep reinforcement learning has shown its success in game playing.
Adaptation and Re-Identification Network: An Unsupervised Deep Transfer Learning Approach to Person Re-Identification
Person re-identification (Re-ID) aims at recognizing the same person from images taken across different cameras.
Ranked #20 on
Unsupervised Domain Adaptation
on Duke to Market
Deep Learning for Malicious Flow Detection
We also conduct a partial flow experiment which shows the feasibility of real-time detection and a zero-shot learning experiment which justifies the generalization capability of deep learning in cyber security.
