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Found 30 papers, 15 papers with code
Image-Text Co-Decomposition for Text-Supervised Semantic Segmentation
This paper addresses text-supervised semantic segmentation, aiming to learn a model capable of segmenting arbitrary visual concepts within images by using only image-text pairs without dense annotations.
DoRA: Weight-Decomposed Low-Rank Adaptation
By employing DoRA, we enhance both the learning capacity and training stability of LoRA while avoiding any additional inference overhead.
SemPLeS: Semantic Prompt Learning for Weakly-Supervised Semantic Segmentation
In this way, SemPLeS can perform better semantic alignment between object regions and the associated class labels, resulting in desired pseudo masks for training the segmentation model.
PartDistill: 3D Shape Part Segmentation by Vision-Language Model Distillation
This paper proposes a cross-modal distillation framework, PartDistill, which transfers 2D knowledge from vision-language models (VLMs) to facilitate 3D shape part segmentation.
Conditional Modeling Based Automatic Video Summarization
The aim of video summarization is to shorten videos automatically while retaining the key information necessary to convey the overall story.
2D-3D Interlaced Transformer for Point Cloud Segmentation with Scene-Level Supervision
Considering the high annotation cost of point clouds, effective 2D and 3D feature fusion based on weakly supervised learning is in great demand.
Probabilistic 3D Multi-Object Cooperative Tracking for Autonomous Driving via Differentiable Multi-Sensor Kalman Filter
However, their proposed methods mainly use cooperative detection results as input to a standard single-sensor Kalman Filter-based tracking algorithm.
Frequency-Aware Self-Supervised Long-Tailed Learning
Data collected from the real world typically exhibit long-tailed distributions, where frequent classes contain abundant data while rare ones have only a limited number of samples.
Learning Continuous Exposure Value Representations for Single-Image HDR Reconstruction
To address this, we propose the continuous exposure value representation (CEVR), which uses an implicit function to generate LDR images with arbitrary EVs, including those unseen during training.
QuAVF: Quality-aware Audio-Visual Fusion for Ego4D Talking to Me Challenge
This technical report describes our QuAVF@NTU-NVIDIA submission to the Ego4D Talking to Me (TTM) Challenge 2023.
A Closer Look at Geometric Temporal Dynamics for Face Anti-Spoofing
Face anti-spoofing (FAS) is indispensable for a face recognition system.
Causalainer: Causal Explainer for Automatic Video Summarization
In this work, a Causal Explainer, dubbed Causalainer, is proposed to address this issue.
Interaction-Aware Prompting for Zero-Shot Spatio-Temporal Action Detection
Finally, we calculate the similarity between the interaction feature and the text feature for each label to determine the action category.
Kinship Representation Learning with Face Componential Relation
Kinship recognition aims to determine whether the subjects in two facial images are kin or non-kin, which is an emerging and challenging problem.
MicroISP: Processing 32MP Photos on Mobile Devices with Deep Learning
While neural networks-based photo processing solutions can provide a better image quality compared to the traditional ISP systems, their application to mobile devices is still very limited due to their very high computational complexity.
PyNet-V2 Mobile: Efficient On-Device Photo Processing With Neural Networks
The increased importance of mobile photography created a need for fast and performant RAW image processing pipelines capable of producing good visual results in spite of the mobile camera sensor limitations.
Holistic Interaction Transformer Network for Action Detection
Actions are about how we interact with the environment, including other people, objects, and ourselves.
Ranked #1 on
Action Detection
on MultiSports
Self-Supervised Robustifying Guidance for Monocular 3D Face Reconstruction
Therefore, we propose a Self-Supervised RObustifying GUidancE (ROGUE) framework to obtain robustness against occlusions and noise in the face images.
Learned Smartphone ISP on Mobile NPUs with Deep Learning, Mobile AI 2021 Challenge: Report
As the quality of mobile cameras starts to play a crucial role in modern smartphones, more and more attention is now being paid to ISP algorithms used to improve various perceptual aspects of mobile photos.
Network Space Search for Pareto-Efficient Spaces
We propose an NSS method to directly search for efficient-aware network spaces automatically, reducing the manual effort and immense cost in discovering satisfactory ones.
Action Segmentation with Mixed Temporal Domain Adaptation
The main progress for action segmentation comes from densely-annotated data for fully-supervised learning.
Ranked #14 on
Action Segmentation
on Breakfast
Action Segmentation with Joint Self-Supervised Temporal Domain Adaptation
Despite the recent progress of fully-supervised action segmentation techniques, the performance is still not fully satisfactory.
Ranked #11 on
Action Segmentation
on GTEA
Interpretable Self-Attention Temporal Reasoning for Driving Behavior Understanding
Performing driving behaviors based on causal reasoning is essential to ensure driving safety.
Traffic Sign Detection under Challenging Conditions: A Deeper Look Into Performance Variations and Spectral Characteristics
We investigate the effect of challenging conditions through spectral analysis and show that challenging conditions can lead to distinct magnitude spectrum characteristics.
Temporal Attentive Alignment for Large-Scale Video Domain Adaptation
Finally, we propose Temporal Attentive Adversarial Adaptation Network (TA3N), which explicitly attends to the temporal dynamics using domain discrepancy for more effective domain alignment, achieving state-of-the-art performance on four video DA datasets (e. g. 7. 9% accuracy gain over "Source only" from 73. 9% to 81. 8% on "HMDB --> UCF", and 10. 3% gain on "Kinetics --> Gameplay").
Image Captioning with Integrated Bottom-Up and Multi-level Residual Top-Down Attention for Game Scene Understanding
However, little work has been done for game image captioning which has some unique characteristics and requirements.
Temporal Attentive Alignment for Video Domain Adaptation
Finally, we propose Temporal Attentive Adversarial Adaptation Network (TA3N), which explicitly attends to the temporal dynamics using domain discrepancy for more effective domain alignment, achieving state-of-the-art performance on three video DA datasets.
Ranked #1 on
Domain Adaptation
on UCF-to-Olympic
Challenging Environments for Traffic Sign Detection: Reliability Assessment under Inclement Conditions
Experimental results show that benchmarked algorithms are highly sensitive to tested challenging conditions, which result in an average performance drop of 0. 17 in terms of precision and a performance drop of 0. 28 in recall under severe conditions.
TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity Recognition
We demonstrate that using both RNNs (using LSTMs) and Temporal-ConvNets on spatiotemporal feature matrices are able to exploit spatiotemporal dynamics to improve the overall performance.
Ranked #54 on
Action Recognition
on UCF101
Depth and Skeleton Associated Action Recognition without Online Accessible RGB-D Cameras
Our approach aims to enhance action recognition in RGB videos by leveraging the extra database.
