Meetings

iCVL Meetings

The iCVL maintains its own weekly seminar series and reading group where we either have guests to discuss their research or we have one of our own lead the study of a topic or a specific research of interest from the literature. Our meetings run as two hour round table discussions (currently on Wednesdays 10:00-12:00) and enjoy a significant level of interaction than most seminars. We will be delighted to host you to learn about your work too. Please contact the lab’s director or our seminar coordinator.

While group meetings started in 2007, organized listings for the web site began in Feb 2012, shortly before its launching in July 2012. The forthcoming meeting is automatically highlighted and centered below. Please scroll up and down for future and past meetings.

2020
  • 24.06.2020
  • VisualComputing Seminar
  • TBA
Abstract: TBA
  • 17.06.2020
  • Seminar Slot
  • TBA
Abstract: TBA
  • 10.06.2020
  • iCVL Group
  • Monthly Research Status Meeting
Abstract: Each iCVL team member will give an update on the status of his last month's actions, raise issues for discussion and brief consultation, and present his action items for the coming month.
  • 03.06.2020
  • Seminar Slot
  • TBA
Abstract: TBA
  • 20.05.2020
  • VisualComputing Seminar
  • TBA
Abstract: TBA
  • 13.05.2020
  • Seminar Slot
  • TBA
Abstract: TBA
  • 06.05.2020
  • iCVL Group
  • Monthly Research Status Meeting
Abstract: Each iCVL team member will give an update on the status of his last month's actions, raise issues for discussion and brief consultation, and present his action items for the coming month.
  • 22.04.2020
  • VisualComputing Seminar
  • TBA
Abstract: TBA
  • 01.04.2020
  • Seminar Slot
  • TBA
Abstract: TBA
  • 25.03.2020
  • VisualComputing Seminar
  • TBA
Abstract: TBA
  • 18.03.2020
  • iCVL Group
  • Monthly Reading Group
Abstract: In this meeting we will be discussing the following paper:
  • 11.03.2020
  • iCVL Group
  • Monthly Research Status Meeting
Abstract: Each iCVL team member will give an update on the status of his last month's actions, raise issues for discussion and brief consultation, and present his action items for the coming month.
  • 04.03.2020
  • Guy Amit - BGU
  • Neural Network Representation Control: Gaussian Isolation Machines and CVC Regularization
Abstract: In many cases, neural network classifiers are likely to be exposed to input data that is outside of their training distribution data. Samples from outside the distribution may be classified as an existing class with high probability by softmax-based classifiers; such incorrect classifications affect the performance of the classifiers and the applications/systems that depend on them. Previous research aimed at distinguishing training distribution data from out-of-distribution data (OOD) has proposed detectors that are external to the classification method. We present Gaussian isolation machine (GIM), a novel hybrid (generative-discriminative) classifier aimed at solving the problem arising when OOD data is encountered. The GIM is based on a neural network and utilizes a new loss function that imposes a distribution on each of the trained classes in the neural network's output space, which can be approximated by a Gaussian. The proposed GIM's novelty lies in its discriminative performance and generative capabilities, a combination of characteristics not usually seen in a single classifier. The GIM achieves state-of-the-art classification results on image recognition and sentiment analysis benchmarking datasets and can also deal with OOD inputs. We also demonstrate the benefits of incorporating part of the GIM's loss function into standard neural networks as a regularization method.  


The paper can be found on arXiv:
   https://arxiv.org/pdf/2002.02176.pdf  
  • 26.02.2020
  • Seminar Slot
  • TBA
Abstract: TBA
  • 19.02.2020
  • iCVL Group
  • Monthly Reading Group
Abstract: In this meeting we will be discussing the following paper:
Viola, P., & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. CVPR (1)1(511-518), 3.‏  
  • 12.02.2020
  • iCVL Group
  • Monthly Research Status Meeting
Abstract: Each iCVL team member will give an update on the status of his last month's actions, raise issues for discussion and brief consultation, and present his action items for the coming month.
  • 22.01.2020
  • VisualComputing Seminar: Oran Shayer - BMW
  • Enhancing Generic Segmentation with Learned Region Representations
Abstract:

 Current successful approaches for generic (non-semantic) segmentation rely mostly on edge detection and have leveraged the strengths of deep learning mainly by improving the edge detection stage in the algorithmic pipeline. This is in contrast to semantic and instance segmentation, where deep learning has made a dramatic affect and DNNs are applied directly to generate pixel-wise segment representations. We propose a new method for learning a pixel-wise region representation that reflects segment relatedness. This representation is combined with an edge map to yield a new segmentation algorithm. We show that the representations themselves achieve state-of-the-art segment similarity scores. Moreover, the proposed, combined segmentation algorithm provides results that are either the state of the art or improve it, for most quality measures.


Bio:
Oran holds a BSc and MSc in EE from the Technion, majoring in machine learning, computer vision and deep learning. In the last 10 years, Oran worked at various companies like Apple, Intel and GM, and also experienced in the startup scene working for Clair Labs. He currently holds a position of machine learning researcher at BMW.
  • 15.01.2020
  • Roy Uziel & Meitar Ronen - BGU
  • Bayesian Adaptive Superpixel Segmentation
Abstract:  Roy and Meitar will present their ICCV-2019 paper, Bayesian Adaptive Superpixel Segmentationhttps://www.cs.bgu.ac.il/~orenfr/BASS/Uziel_ICCV_2019.pdf

Superpixels provide a useful intermediate image representation. Existing superpixel methods, however, suffer from at least some of the following drawbacks: 1) topology is handled heuristically; 2) the number of superpixels is either predefined or estimated at a prohibitive cost; 3) lack of adaptiveness. As a remedy, we propose a novel probabilistic model, self-coined Bayesian Adaptive Superpixel Segmentation (BASS), together with an efficient inference. BASS is a Bayesian nonparametric mixture model that also respects topology and favors spatial coherence. The optimization based and topology-aware inference is parallelizable and implemented in GPU. Quantitatively, BASS achieves results that are either better than the state-of-the-art or close to it, depending on the performance index and/or dataset. Qualitatively, we argue it achieves the best results; we demonstrate this by not only subjective visual inspection but also objective quantitative performance evaluation of the downstream application of face detection.  
  • 08.01.2020
  • iCVL Group
  • Monthly Reading Group
Abstract: In this meeting we will be discussing the following paper: Lowe, D. G. (1999, September). Object recognition from local scale-invariant features. In iccv (Vol. 99, No. 2, pp. 1150-1157).‏
  • 01.01.2020
  • iCVL Group
  • Monthly Research Status Meeting
Abstract: Each iCVL team member will give an update on the status of his last month's actions, raise issues for discussion and brief consultation, and present his action items for the coming month.
2019
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