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Replay-Based Online Adaptation for Unsupervised Deep Visual Odometry
(2024)Lecture Notes in Computer Science ~ Progress in Pattern Recognition, Image Analysis, Computer Vision, and ApplicationsOnline adaptation is a promising paradigm that enables dynamic adaptation to new environments. In recent years, there has been a growing interest in exploring online adaptation for various problems, including visual odometry, a crucial task in robotics, autonomous systems, and driver assistance applications. In this work, we leverage experience replay, a potent technique for enhancing online adaptation, to explore the replay-based online ...Conference Paper -
MS-EVS: Multispectral event-based vision for deep learning based face detection
(2024)2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)Event-based sensing is a relatively new imaging modality that enables low latency, low power, high temporal resolution and high dynamic range acquisition. These properties make it a highly desirable sensor for edge applications and in high dynamic range environments. As of today, most event-based sensors are monochromatic (grayscale), capturing light from a wide spectral range over the visible, in a single channel. In this paper, we ...Conference Paper -
Optimizing Long-Term Robot Tracking with Multi-Platform Sensor Fusion
(2024)2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)Monitoring a fleet of robots requires stable long-term tracking with re-identification, which is yet an unsolved challenge in many scenarios. One application of this is the analysis of autonomous robotic soccer games at RoboCup. Tracking in these games requires handling of identically looking players, strong occlusions, and non-professional video recordings, but also offers state information estimated by the robots. In order to make ...Conference Paper -
Beyond SOT: Tracking Multiple Generic Objects at Once
(2024)2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)Generic Object Tracking (GOT) is the problem of tracking target objects, specified by bounding boxes in the first frame of a video. While the task has received much attention in the last decades, researchers have almost exclusively focused on the single object setting. However multiobject GOT poses its own challenges and is more attractive in real-world applications. We attribute the lack of research interest into this problem to the ...Conference Paper -
Contrastive Learning for Multi-Object Tracking with Transformers
(2024)2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)The DEtection TRansformer (DETR) opened new possibilities for object detection by modeling it as a translation task: converting image features into object-level representations. Previous works typically add expensive modules to DETR to perform Multi-Object Tracking (MOT), resulting in more complicated architectures. We instead show how DETR can be turned into a MOT model by employing an instance-level contrastive loss, a revised sampling ...Conference Paper -
2D Feature Distillation for Weakly- and Semi-Supervised 3D Semantic Segmentation
(2024)2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)As 3D perception problems grow in popularity and the need for large-scale labeled datasets for LiDAR semantic segmentation increase, new methods arise that aim to reduce the necessity for dense annotations by employing weakly-supervised training. However these methods continue to show weak boundary estimation and high false negative rates for small objects and distant sparse regions. We argue that such weaknesses can be compensated by ...Conference Paper -
StyleGenes: Discrete and Efficient Latent Distributions for GANs
(2024)2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)We propose a discrete latent distribution for Generative Adversarial Networks (GANs). Instead of drawing latent vectors from a continuous prior, we sample from a finite set of learnable latents. However, a direct parametrization of such a distribution leads to an intractable linear increase in memory in order to ensure sufficient sample diversity. We address this key issue by taking inspiration from the encoding of information in biological ...Conference Paper -
MultiVT: Multiple-Task Framework for Dentistry
(2024)Lecture Notes in Computer Science ~ Domain Adaptation and Representation TransferCurrent image understanding methods on dental data are often trained end-to-end on inputs and labels, with focus on using state-of-the-art neural architectures. Such approaches, however, typically ignore domain specific peculiarities and lack the ability to generalize outside their training dataset. We observe that, in RGB images, teeth display a weak or unremarkable texture while exhibiting strong boundaries; similarly, in panoramic ...Conference Paper -
LocalViT: Analyzing Locality in Vision Transformers
(2023)2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)The aim of this paper is to study the influence of locality mechanisms in vision transformers. Transformers originated from machine translation and are particularly good at modelling long-range dependencies within a long sequence. Although the global interaction between the token embeddings could be well modelled by the self-attention mechanism of transformers, what is lacking is a locality mechanism for information exchange within a local ...Conference Paper -
Model-aware 3D Eye Gaze from Weak and Few-shot Supervisions
(2023)2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)The task of predicting 3D eye gaze from eye images can be performed either by (a) end-to-end learning for image-to-gaze mapping or by (b) fitting a 3D eye model onto images. The former case requires 3D gaze labels, while the latter requires eye semantics or landmarks to facilitate the model fitting. Although obtaining eye semantics and landmarks is relatively easy, fitting an accurate 3D eye model on them remains to be very challenging ...Conference Paper