The crucial need for reliable, robust and un-biased biodiversity data in support of initiatives such as the 30x30 initiative, which aims to conserve 30% of the world’s oceans by 2030, presents significant scientific and technological challenges. There have been advances made to automate fish biodiversity assessments using computer vision. However, the stark difference in research fields between ecology and artificial intelligence hinders the efficient use of computer vision tools for ecological tasks. This demo presents CleverFish, a novel tool designed to bridge the gap between artificial intelligence and marine biology. CleverFish tackles three core challenges of an efficient management tool: i) providing an easy-to-use graphical user interface to an AI pipeline, ii) accommodating global and video-specific in-app biodiversity assessment and iii) allowing fast and efficient extraction of temporal and spatial fish species distribution in a format understandable for ecologists. An accessible web application enables seamless integration into marine monitoring pipelines and conservation efforts.

Concept-based models have been proposed as a new line of research for explainable by-design deep learning models. However, those models show their whole power when applied to benchmarks where the concepts are well defined and the concepts' attributes easily extractable from the raw data. In this paper, we challenge the most recent concept-based model initially developed for image classification, on more complex interpretative tasks from a recently proposed video benchmark where they perform poorly. We conduct a root cause analysis of the poor performances of state-of-the-art explainable concept-based models for these multimodal interpretative tasks, and propose adaptations to design robust explainable models for detecting character objectification in this novel challenging video benchmark. We show that the optimal architectural choice may vary depending on the modality setting, thereby showing that designing multimodal concept-based approaches remains an open challenge and calls for further investigation.

While HDMaps are a crucial component of autonomous driving, they are expensive to acquire and maintain. Estimating these maps from sensors therefore promises to significantly lighten costs. These estimations however overlook existing HDMaps, with current methods at most geolocalizing low quality maps or considering a general database of known maps. In this paper, we propose to account for existing maps of the precise situation studied when estimating HDMaps. To prove this, we identify 3 reasonable types of useful existing maps (minimalist, noisy, and outdated). We then introduce MapEX, a novel online HDMap estimation framework that accounts for existing maps. MapEX achieves this by encoding map elements into query tokens and by refining the matching algorithm used to train classic query based map estimation models. We demonstrate that MapEX brings significant improvements on the nuScenes dataset. For instance, MapEX - given noisy maps - improves by 38% over the MapTRv2 detector it is based on and by 8% over the current SOTA.

Deep architecture have proven capable of solving many tasks provided a sufficient amount of labeled data. In fact, the amount of available labeled data has become the principal bottleneck in low label settings such as Semi-Supervised Learning. Mixing Data Augmentations do not typically yield new labeled samples, as indiscriminately mixing contents creates between-class samples. In this work, we introduce the SciMix framework that can learn to replace the global semantic content from one sample. By teaching a StyleGan generator to embed a semantic style code into image backgrounds, we obtain new mixing scheme for data augmentation. We then demonstrate that SciMix yields novel mixed samples that inherit many characteristics from their non-semantic parents. Afterwards, we verify those samples can be used to improve the performance semi-supervised frameworks like Mean Teacher or Fixmatch, and even fully supervised learning on a small labeled dataset.

Generative models can be trained to emulate complex empirical data, but are they useful to make predictions in the context of previously unobserved environments? An intuitive idea to promote such extrapolation capabilities is to have the architecture of such model reflect a causal graph of the true data generating process, such that one can intervene on each node independently of the others. However, the nodes of this graph are usually unobserved, leading to overparameterization and lack of identifiability of the causal structure. We develop a theoretical framework to address this challenging situation by defining a weaker form of identifiability, based on the principle of independence of mechanisms. We demonstrate on toy examples that classical stochastic gradient descent can hinder the model's extrapolation capabilities, suggesting independence of mechanisms should be enforced explicitly during training. Experiments on deep generative models trained on real world data support these insights and illustrate how the extrapolation capabilities of such models can be leveraged.

Recent strategies achieved ensembling "for free" by fitting concurrently diverse subnetworks inside a single base network. The main idea during training is that each sub-network learns to classify only one of the multiple inputs simultaneously provided. However, the question of how to best mix these multiple inputs has not been studied so farIn this paper, we introduce MixMo, a new generalized framework for learning multi-input multi-output deep subnetworks. Our key motivation is to replace the suboptimal summing operation hidden in previous approaches by a more appropriate mixing mechanism. For that purpose, we draw inspiration from successful mixed sample data augmentations. We show that binary mixing in features - particularly with rectangular patches from CutMix - enhances results by making subnetworks stronger and more diverse.We improve state of the art for image classification on CIFAR-100 and Tiny ImageNet datasets. Our easy to implement models notably outperform data augmented deep ensembles, without the inference and memory overheads. As we operate in features and simply better leverage the expressiveness of large networks, we open a new line of research complementary to previous works.

, i.e. on input data from a different distribution than what it was trained for. This is an important problem to solve on the road towards creating reliable computer vision systems for real-world applications, because the quality of a classifier's predictions cannot be guaranteed if it operates out-of-specs. Previously proposed methods for out-of-specs detection make decisions on the level of single inputs. This, however, is insufficient to achieve low false positive rate and high false negative rates at the same time. In this work, we describe a new procedure named KS(conf), based on statistical reasoning. Its main component is a classical Kolmogorov-Smirnov test that is applied to the set of predicted confidence values for batches of samples. Working with batches instead of single samples allows increasing the true positive rate without negatively affecting the false positive rate, thereby overcoming a crucial limitation of single sample tests. We show by extensive experiments using a variety of convolutional network architectures and datasets that KS(conf) reliably detects out-of-specs situations even under conditions where other tests fail. It furthermore has a number of properties that make it an excellent candidate for practical deployment: it is easy to implement, adds almost no overhead to the system, works with any classifier that outputs confidence scores, and requires no a priori knowledge about how the data distribution could change.

The original version of this article contained a mistake in the denominator of equation (1).