[{"external_id":{"arxiv":["2004.12623"]},"date_published":"2020-03-01T00:00:00Z","type":"conference","publisher":"IEEE","month":"03","status":"public","language":[{"iso":"eng"}],"quality_controlled":"1","day":"01","oa":1,"related_material":{"record":[{"relation":"dissertation_contains","status":"deleted","id":"8331"},{"id":"8390","status":"public","relation":"dissertation_contains"}]},"author":[{"first_name":"Amélie","last_name":"Royer","orcid":"0000-0002-8407-0705","id":"3811D890-F248-11E8-B48F-1D18A9856A87","full_name":"Royer, Amélie"},{"last_name":"Lampert","first_name":"Christoph","full_name":"Lampert, Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-8622-7887"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","department":[{"_id":"ChLa"}],"publication":"IEEE Winter Conference on Applications of Computer Vision","date_updated":"2023-09-07T13:16:17Z","arxiv":1,"title":"Localizing grouped instances for efficient detection in low-resource scenarios","publication_status":"published","oa_version":"Preprint","article_processing_charge":"No","main_file_link":[{"url":"https://arxiv.org/abs/2004.12623","open_access":"1"}],"doi":"10.1109/WACV45572.2020.9093288","scopus_import":1,"publication_identifier":{"isbn":["9781728165530"]},"conference":{"location":" Snowmass Village, CO, United States","name":"WACV: Winter Conference on Applications of Computer Vision","end_date":"2020-03-05","start_date":"2020-03-01"},"_id":"7936","date_created":"2020-06-07T22:00:53Z","year":"2020","article_number":"1716-1725","citation":{"ieee":"A. Royer and C. Lampert, “Localizing grouped instances for efficient detection in low-resource scenarios,” in <i>IEEE Winter Conference on Applications of Computer Vision</i>,  Snowmass Village, CO, United States, 2020.","ista":"Royer A, Lampert C. 2020. Localizing grouped instances for efficient detection in low-resource scenarios. IEEE Winter Conference on Applications of Computer Vision. WACV: Winter Conference on Applications of Computer Vision, 1716–1725.","ama":"Royer A, Lampert C. Localizing grouped instances for efficient detection in low-resource scenarios. In: <i>IEEE Winter Conference on Applications of Computer Vision</i>. IEEE; 2020. doi:<a href=\"https://doi.org/10.1109/WACV45572.2020.9093288\">10.1109/WACV45572.2020.9093288</a>","short":"A. Royer, C. Lampert, in:, IEEE Winter Conference on Applications of Computer Vision, IEEE, 2020.","apa":"Royer, A., &#38; Lampert, C. (2020). Localizing grouped instances for efficient detection in low-resource scenarios. In <i>IEEE Winter Conference on Applications of Computer Vision</i>.  Snowmass Village, CO, United States: IEEE. <a href=\"https://doi.org/10.1109/WACV45572.2020.9093288\">https://doi.org/10.1109/WACV45572.2020.9093288</a>","chicago":"Royer, Amélie, and Christoph Lampert. “Localizing Grouped Instances for Efficient Detection in Low-Resource Scenarios.” In <i>IEEE Winter Conference on Applications of Computer Vision</i>. IEEE, 2020. <a href=\"https://doi.org/10.1109/WACV45572.2020.9093288\">https://doi.org/10.1109/WACV45572.2020.9093288</a>.","mla":"Royer, Amélie, and Christoph Lampert. “Localizing Grouped Instances for Efficient Detection in Low-Resource Scenarios.” <i>IEEE Winter Conference on Applications of Computer Vision</i>, 1716–1725, IEEE, 2020, doi:<a href=\"https://doi.org/10.1109/WACV45572.2020.9093288\">10.1109/WACV45572.2020.9093288</a>."},"abstract":[{"lang":"eng","text":"State-of-the-art detection systems are generally evaluated on their ability to exhaustively retrieve objects densely distributed in the image, across a wide variety of appearances and semantic categories. Orthogonal to this, many real-life object detection applications, for example in remote sensing, instead require dealing with large images that contain only a few small objects of a single class, scattered heterogeneously across the space. In addition, they are often subject to strict computational constraints, such as limited battery capacity and computing power.To tackle these more practical scenarios, we propose a novel flexible detection scheme that efficiently adapts to variable object sizes and densities: We rely on a sequence of detection stages, each of which has the ability to predict groups of objects as well as individuals. Similar to a detection cascade, this multi-stage architecture spares computational effort by discarding large irrelevant regions of the image early during the detection process. The ability to group objects provides further computational and memory savings, as it allows working with lower image resolutions in early stages, where groups are more easily detected than individuals, as they are more salient. We report experimental results on two aerial image datasets, and show that the proposed method is as accurate yet computationally more efficient than standard single-shot detectors, consistently across three different backbone architectures."}]},{"date_created":"2020-06-07T22:00:53Z","_id":"7937","conference":{"name":"WACV: Winter Conference on Applications of Computer Vision","location":"Snowmass Village, CO, United States","end_date":"2020-03-05","start_date":"2020-03-01"},"abstract":[{"text":"Fine-tuning is a popular way of exploiting knowledge contained in a pre-trained convolutional network for a new visual recognition task. However, the orthogonal setting of transferring knowledge from a pretrained network to a visually different yet semantically close source is rarely considered: This commonly happens with real-life data, which is not necessarily as clean as the training source (noise, geometric transformations, different modalities, etc.).To tackle such scenarios, we introduce a new, generalized form of fine-tuning, called flex-tuning, in which any individual unit (e.g. layer) of a network can be tuned, and the most promising one is chosen automatically. In order to make the method appealing for practical use, we propose two lightweight and faster selection procedures that prove to be good approximations in practice. We study these selection criteria empirically across a variety of domain shifts and data scarcity scenarios, and show that fine-tuning individual units, despite its simplicity, yields very good results as an adaptation technique. As it turns out, in contrast to common practice, rather than the last fully-connected unit it is best to tune an intermediate or early one in many domain- shift scenarios, which is accurately detected by flex-tuning.","lang":"eng"}],"citation":{"short":"A. Royer, C. Lampert, in:, 2020 IEEE Winter Conference on Applications of Computer Vision, IEEE, 2020.","apa":"Royer, A., &#38; Lampert, C. (2020). A flexible selection scheme for minimum-effort transfer learning. In <i>2020 IEEE Winter Conference on Applications of Computer Vision</i>. Snowmass Village, CO, United States: IEEE. <a href=\"https://doi.org/10.1109/WACV45572.2020.9093635\">https://doi.org/10.1109/WACV45572.2020.9093635</a>","chicago":"Royer, Amélie, and Christoph Lampert. “A Flexible Selection Scheme for Minimum-Effort Transfer Learning.” In <i>2020 IEEE Winter Conference on Applications of Computer Vision</i>. IEEE, 2020. <a href=\"https://doi.org/10.1109/WACV45572.2020.9093635\">https://doi.org/10.1109/WACV45572.2020.9093635</a>.","mla":"Royer, Amélie, and Christoph Lampert. “A Flexible Selection Scheme for Minimum-Effort Transfer Learning.” <i>2020 IEEE Winter Conference on Applications of Computer Vision</i>, 2180–2189, IEEE, 2020, doi:<a href=\"https://doi.org/10.1109/WACV45572.2020.9093635\">10.1109/WACV45572.2020.9093635</a>.","ieee":"A. Royer and C. Lampert, “A flexible selection scheme for minimum-effort transfer learning,” in <i>2020 IEEE Winter Conference on Applications of Computer Vision</i>, Snowmass Village, CO, United States, 2020.","ista":"Royer A, Lampert C. 2020. A flexible selection scheme for minimum-effort transfer learning. 2020 IEEE Winter Conference on Applications of Computer Vision. WACV: Winter Conference on Applications of Computer Vision, 2180–2189.","ama":"Royer A, Lampert C. A flexible selection scheme for minimum-effort transfer learning. In: <i>2020 IEEE Winter Conference on Applications of Computer Vision</i>. IEEE; 2020. doi:<a href=\"https://doi.org/10.1109/WACV45572.2020.9093635\">10.1109/WACV45572.2020.9093635</a>"},"year":"2020","article_number":"2180-2189","oa_version":"Preprint","article_processing_charge":"No","title":"A flexible selection scheme for minimum-effort transfer learning","publication_status":"published","publication_identifier":{"isbn":["9781728165530"]},"scopus_import":"1","doi":"10.1109/WACV45572.2020.9093635","main_file_link":[{"open_access":"1","url":"http://arxiv.org/abs/2008.11995"}],"date_updated":"2023-09-07T13:16:17Z","publication":"2020 IEEE Winter Conference on Applications of Computer Vision","department":[{"_id":"ChLa"}],"arxiv":1,"related_material":{"record":[{"id":"8331","status":"deleted","relation":"dissertation_contains"},{"status":"public","relation":"dissertation_contains","id":"8390"}]},"oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"first_name":"Amélie","last_name":"Royer","id":"3811D890-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-8407-0705","full_name":"Royer, Amélie"},{"first_name":"Christoph","last_name":"Lampert","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-8622-7887","full_name":"Lampert, Christoph"}],"quality_controlled":"1","day":"01","status":"public","month":"03","language":[{"iso":"eng"}],"type":"conference","date_published":"2020-03-01T00:00:00Z","external_id":{"arxiv":["2008.11995"]},"publisher":"IEEE"}]