MultIOD: Rehearsal-free Multihead Incremental Object Detector

Class-Incremental learning (CIL) is the ability of artificial agents to accommodate new classes as they appear in a stream. It is particularly interesting in evolving environments where agents have limited access to memory and computational resources. The main challenge of class-incremental learning is catastrophic forgetting, the inability of neural networks to retain past knowledge when learning a new one. Unfortunately, most existing class-incremental object detectors are applied to two-stage algorithms such as Faster-RCNN and rely on rehearsal memory to retain past knowledge. We believe that the current benchmarks are not realistic, and more effort should be dedicated to anchor-free and rehearsal-free object detection. In this context, we propose MultIOD, a class-incremental object detector based on CenterNet. Our main contributions are: (1) we propose a multihead feature pyramid and multihead detection architecture to efficiently separate class representations, (2) we employ transfer learning between classes learned initially and those learned incrementally to tackle catastrophic forgetting, and (3) we use a class-wise non-max-suppression as a post-processing technique to remove redundant boxes. Without bells and whistles, our method outperforms a range of state-of-the-art methods on two Pascal VOC datasets.Comment: Under review at the WACV 2024 conferenc

Gradient-Based Post-Training Quantization: Challenging the Status Quo

Quantization has become a crucial step for the efficient deployment of deep neural networks, where floating point operations are converted to simpler fixed point operations. In its most naive form, it simply consists in a combination of scaling and rounding transformations, leading to either a limited compression rate or a significant accuracy drop. Recently, Gradient-based post-training quantization (GPTQ) methods appears to be constitute a suitable trade-off between such simple methods and more powerful, yet expensive Quantization-Aware Training (QAT) approaches, particularly when attempting to quantize LLMs, where scalability of the quantization process is of paramount importance. GPTQ essentially consists in learning the rounding operation using a small calibration set. In this work, we challenge common choices in GPTQ methods. In particular, we show that the process is, to a certain extent, robust to a number of variables (weight selection, feature augmentation, choice of calibration set). More importantly, we derive a number of best practices for designing more efficient and scalable GPTQ methods, regarding the problem formulation (loss, degrees of freedom, use of non-uniform quantization schemes) or optimization process (choice of variable and optimizer). Lastly, we propose a novel importance-based mixed-precision technique. Those guidelines lead to significant performance improvements on all the tested state-of-the-art GPTQ methods and networks (e.g. +6.819 points on ViT for 4-bit quantization), paving the way for the design of scalable, yet effective quantization methods

PIPE : Parallelized Inference Through Post-Training Quantization Ensembling of Residual Expansions

Deep neural networks (DNNs) are ubiquitous in computer vision and natural language processing, but suffer from high inference cost. This problem can be addressed by quantization, which consists in converting floating point perations into a lower bit-width format. With the growing concerns on privacy rights, we focus our efforts on data-free methods. However, such techniques suffer from their lack of adaptability to the target devices, as a hardware typically only support specific bit widths. Thus, to adapt to a variety of devices, a quantization method shall be flexible enough to find good accuracy v.s. speed trade-offs for every bit width and target device. To achieve this, we propose PIPE, a quantization method that leverages residual error expansion, along with group sparsity and an ensemble approximation for better parallelization. PIPE is backed off by strong theoretical guarantees and achieves superior performance on every benchmarked application (from vision to NLP tasks), architecture (ConvNets, transformers) and bit-width (from int8 to ternary quantization).Comment: arXiv admin note: substantial text overlap with arXiv:2203.1464

Multiple kernel learning SVM and statistical validation for facial landmark detection

Abstract — In this paper we present a robust and accurate method to detect 17 facial landmarks in expressive face images. We introduce a new multi-resolution framework based on the recent multiple kernel algorithm. Low resolution patches carry the global information of the face and give a coarse but robust detection of the desired landmark. High resolution patches, using local details, refine this location. This process is combined with a bootstrap process and a statistical validation, both improving the system robustness. Combining independent point detection and prior knowledge on the point distribution, the proposed detector is robust to variable lighting conditions and facial expressions. This detector is tested on several databases and the results reported can be compared favorably with the current state of the art point detectors. I

REx: Data-Free Residual Quantization Error Expansion

Deep neural networks (DNNs) are ubiquitous in computer vision and natural language processing, but suffer from high inference cost. This problem can be addressed by quantization, which consists in converting floating point operations into a lower bit-width format. With the growing concerns on privacy rights, we focus our efforts on data-free methods. However, such techniques suffer from their lack of adaptability to the target devices, as a hardware typically only support specific bit widths. Thus, to adapt to a variety of devices, a quantization method shall be flexible enough to find good accuracy v.s. speed trade-offs for every bit width and target device. To achieve this, we propose REx, a quantization method that leverages residual error expansion, along with group sparsity and an ensemble approximation for better parallelization. REx is backed off by strong theoretical guarantees and achieves superior performance on every benchmarked application (from vision to NLP tasks), architecture (ConvNets, transformers) and bit-width (from int8 to ternary quantization)

Archtree: on-the-fly tree-structured exploration for latency-aware pruning of deep neural networks

Deep neural networks (DNNs) have become ubiquitous in addressing a number of problems, particularly in computer vision. However, DNN inference is computationally intensive, which can be prohibitive e.g. when considering edge devices. To solve this problem, a popular solution is DNN pruning, and more so structured pruning, where coherent computational blocks (e.g. channels for convolutional networks) are removed: as an exhaustive search of the space of pruned sub-models is intractable in practice, channels are typically removed iteratively based on an importance estimation heuristic. Recently, promising latency-aware pruning methods were proposed, where channels are removed until the network reaches a target budget of wall-clock latency pre-emptively estimated on specific hardware. In this paper, we present Archtree, a novel method for latency-driven structured pruning of DNNs. Archtree explores multiple candidate pruned sub-models in parallel in a tree-like fashion, allowing for a better exploration of the search space. Furthermore, it involves on-the-fly latency estimation on the target hardware, accounting for closer latencies as compared to the specified budget. Empirical results on several DNN architectures and target hardware show that Archtree better preserves the original model accuracy while better fitting the latency budget as compared to existing state-of-the-art methods.Comment: 10 pages, 7 figure

Robust continuous prediction of human emotions using multiscale dynamic cues

Designing systems able to interact with humans in a natural manner is a complex and far from solved problem. A key aspect of natural interaction is the ability to understand and appropriately respond to human emotions. This paper details our response to the Audio/Visual Emotion Challenge (AVEC’12) whose goal is to continuously predict four affective signals describing human emotions (namely valence, arousal, expectancy and power). The proposed method uses log-magnitude Fourier spectra to extract multiscale dynamic descriptions of signals characterizing global and local face appearance as well as head movements and voice. We perform a kernel regression with very few representative samples selected via a supervised weighted-distance-based clustering, that leads to a high generalization power. For selecting features, we introduce a new correlation-based measure that takes into account a possible delay between the labels and the data and significantly increases robustness. We also propose a particularly fast regressor-level fusion framework to merge systems based on di↵erent modalities. Experiments have proven the e ciency of each key point of the proposed method and we obtain very promising results

Combinaison de Descripteurs Hétérogènes pour la Reconnaissance de Micro-Mouvements Faciaux

Session "Posters"National audienceDans cet article, nous présentons notre réponse au premier challenge international sur la reconnaissance et l'analyse d'émotions faciales (Facial Emotion Recognition and Analysis Challenge). Nous proposons une combinaison de dif- férents types de descripteurs dans le but de détecter de manière automatique, les micro-mouvements faciaux d'un visage. Ce système utilise une Machine à Vecteurs Supports Multi-Noyaux pour chacune des Action Units (AU) que nous désirons détecter. Le premier noyau est calculé en utilisant des histogrammes de motifs binaires locaux de Gabor (ou Local Gabor Binary Pattern, LGBP) via un noyau d'intersection d'histogramme. Le second noyau quant à lui, est crée avec des coefficients de Modèles Actifs d'Apparence via un noyau gaussien. Les sorties de chacune des SVM sont ensuite filtrées dans le but d'inclure l'informa- tion temporelle de la séquence. Afin d'évaluer notre système, nous avons procédé à de nombreuses expérimentations sur plusieurs points clefs de notre méthode. Enfin, nous comparons nos résultats à ceux obtenus par les autres participants au challenge, tout en analysant nos performanche