Long-Term Anticipation of Activities with Cycle Consistency

With the success of deep learning methods in analyzing activities in videos, more attention has recently been focused towards anticipating future activities. However, most of the work on anticipation either analyzes a partially observed activity or predicts the next action class. Recently, new approaches have been proposed to extend the prediction horizon up to several minutes in the future and that anticipate a sequence of future activities including their durations. While these works decouple the semantic interpretation of the observed sequence from the anticipation task, we propose a framework for anticipating future activities directly from the features of the observed frames and train it in an end-to-end fashion. Furthermore, we introduce a cycle consistency loss over time by predicting the past activities given the predicted future. Our framework achieves state-of-the-art results on two datasets: the Breakfast dataset and 50Salads.

Attribute Prototype Network for Zero-Shot Learning

From the beginning of zero-shot learning research, visual attributes have been shown to play an important role. In order to better transfer attribute-based knowledge from known to unknown classes, we argue that an image representation with integrated attribute localization ability would be beneficial for zero-shot learning. To this end, we propose a novel zero-shot representation learning framework that jointly learns discriminative global and local features using only class-level attributes. While a visual-semantic embedding layer learns global features, local features are learned through an attribute prototype network that simultaneously regresses and decorrelates attributes from intermediate features. We show that our locality augmented image representations achieve a new state-of-the-art on three zero-shot learning benchmarks. As an additional benefit, our model points to the visual evidence of the attributes in an image, e.g. for the CUB dataset, confirming the improved attribute localization ability of our image representation. The code will be publicaly available at https://wenjiaxu.github.io/APN-ZSL/.

Kinematic 3D Object Detection in Monocular Video

Perceiving the physical world in 3D is fundamental for self-driving applications. Although temporal motion is an invaluable resource to human vision for detection, tracking, and depth perception, such features have not been thoroughly utilized in modern 3D object detectors. In this work, we propose a novel method for monocular video-based 3D object detection which carefully leverages kinematic motion to improve precision of 3D localization. Specifically, we first propose a novel decomposition of object orientation as well as a self-balancing 3D confidence. We show that both components are critical to enable our kinematic model to work effectively. Collectively, using only a single model, we efficiently leverage 3D kinematics from monocular videos to improve the overall localization precision in 3D object detection while also producing useful by-products of scene dynamics (ego-motion and per-object velocity). We achieve state-of-the-art performance on monocular 3D object detection and the Bird's Eye View tasks within the KITTI self-driving dataset.

Generalized Many-Way Few-Shot Video Classification

Few-shot learning methods operate in low data regimes. The aim is to learn with few training examples per class. Although significant progress has been made in few-shot image classification, few-shot video recognition is relatively unexplored and methods based on 2D CNNs are unable to learn temporal information. In this work we thus develop a simple 3D CNN baseline, surpassing existing methods by a large margin. To circumvent the need of labeled examples, we propose to leverage weakly-labeled videos from a large dataset using tag retrieval followed by selecting the best clips with visual similarities, yielding further improvement. Our results saturate current 5-way benchmarks for few-shot video classification and therefore we propose a new challenging benchmark involving more classes and a mixture of classes with varying supervision.

On Mitigating Random and Adversarial Bit Errors

The design of deep neural network (DNN) accelerators, i.e., specialized hardware for inference, has received considerable attention in past years due to saved cost, area, and energy compared to mainstream hardware. We consider the problem of random and adversarial bit errors in quantized DNN weights stored on accelerator memory. Random bit errors arise when optimizing accelerators for energy efficiency by operating at low voltage. Here, the bit error rate increases exponentially with voltage reduction, causing devastating accuracy drops in DNNs. Additionally, recent work demonstrates attacks on voltage controllers to adversarially reduce voltage. Adversarial bit errors have been shown to be realistic through attacks targeting individual bits in accelerator memory. Besides describing these error models in detail, we make first steps towards DNNs robust to random and adversarial bit errors by explicitly taking bit errors into account during training. Our random or adversarial bit error training improves robustness significantly, potentially leading to more energy-efficient and secure DNN accelerators.

Adversarial Training against Location-Optimized Adversarial Patches

Deep neural networks have been shown to be susceptible to adversarial examples -- small, imperceptible changes constructed to cause mis-classification in otherwise highly accurate image classifiers. As a practical alternative, recent work proposed so-called adversarial patches: clearly visible, but adversarially crafted rectangular patches in images. These patches can easily be printed and applied in the physical world. While defenses against imperceptible adversarial examples have been studied extensively, robustness against adversarial patches is poorly understood. In this work, we first devise a practical approach to obtain adversarial patches while actively optimizing their location within the image. Then, we apply adversarial training on these location-optimized adversarial patches and demonstrate significantly improved robustness on CIFAR10 and GTSRB. Additionally, in contrast to adversarial training on imperceptible adversarial examples, our adversarial patch training does not reduce accuracy.

Normalizing Flows with Multi-Scale Autoregressive Priors

Flow-based generative models are an important class of exact inference models that admit efficient inference and sampling for image synthesis. Owing to the efficiency constraints on the design of the flow layers, e.g. split coupling flow layers in which approximately half the pixels do not undergo further transformations, they have limited expressiveness for modeling long-range data dependencies compared to autoregressive models that rely on conditional pixel-wise generation. In this work, we improve the representational power of flow-based models by introducing channel-wise dependencies in their latent space through multi-scale autoregressive priors (mAR). Our mAR prior for models with split coupling flow layers (mAR-SCF) can better capture dependencies in complex multimodal data. The resulting model achieves state-of-the-art density estimation results on MNIST, CIFAR-10, and ImageNet. Furthermore, we show that mAR-SCF allows for improved image generation quality, with gains in FID and Inception scores compared to state-of-the-art flow-based models.

Long-Tailed Recognition Using Class-Balanced Experts

Classic deep learning methods achieve impressive results in image recognition over large-scale artificially-balanced datasets. However, real-world datasets exhibit highly class-imbalanced distributions. In this work we address the problem of long tail recognition wherein the training set is highly imbalanced and the test set is kept balanced. The key challenges faced by any long tail recognition technique are relative imbalance amongst the classes and data scarcity or unseen concepts for mediumshot or fewshot classes. Existing techniques rely on data-resampling, cost sensitive learning, online hard example mining, reshaping the loss objective and complex memory based models to address this problem. We instead propose an ensemble of experts technique that decomposes the imbalanced problem into multiple balanced classification problems which are more tractable. Our ensemble of experts reaches close to state-of-the-art results and an extended ensemble establishes new state-of-the-art on two benchmarks for long tail recognition. We conduct numerous experiments to analyse the performance of the ensemble, and show that in modern datasets relative imbalance is a harder problem than data scarcity.

A U-Net Based Discriminator for Generative Adversarial Networks

Among the major remaining challenges for generative adversarial networks (GANs) is the capacity to synthesize globally and locally coherent images with object shapes and textures indistinguishable from real images. To target this issue we propose an alternative U-Net based discriminator architecture, borrowing the insights from the segmentation literature. The proposed U-Net based architecture allows to provide detailed per-pixel feedback to the generator while maintaining the global coherence of synthesized images, by providing the global image feedback as well. Empowered by the per-pixel response of the discriminator, we further propose a per-pixel consistency regularization technique based on the CutMix data augmentation, encouraging the U-Net discriminator to focus more on semantic and structural changes between real and fake images. This improves the U-Net discriminator training, further enhancing the quality of generated samples. The novel discriminator improves over the state of the art in terms of the standard distribution and image quality metrics, enabling the generator to synthesize images with varying structure, appearance and levels of detail, maintaining global and local realism. Compared to the BigGAN baseline, we achieve an average improvement of 2.7 FID points across FFHQ, CelebA, and the newly introduced COCO-Animals dataset.