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- Learning Deep Representations of Appearance and Motion for Anomalous Event Detection550
This work proposes Appearance and Motion DeepNet (AMDN) which utilizes deep neural networks to automatically learn feature representations, and introduces a novel double fusion framework, combining both the benefits of traditional early fusion and late fusion strategies.
- Detecting anomalous events in videos by learning deep representations of appearance and motion474
A novel double fusion framework is introduced, combining the benefits of traditional early fusion and late fusion strategies, which is extensively evaluated on publicly available video surveillance datasets including UCSD pedestian, Subway, and Train, showing competitive performance with respect to state of the art approaches.
- Multi-scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation466
This paper addresses the problem of depth estimation from a single still image by designing a novel CNN implementation of mean-field updates for continuous CRFs and demonstrates the effectiveness of the proposed approach and establishes new state of the art results on publicly available datasets.
- Animating Arbitrary Objects via Deep Motion Transfer400
This paper introduces a novel deep learning framework for image animation that generates a video in which the target object is animated according to the driving sequence through a deep architecture that decouples appearance and motion information.
- Modeling the Background for Incremental Learning in Semantic Segmentation386
This work revisits classical incremental learning methods, and proposes a new distillation-based framework which explicitly accounts for a semantic distribution shift, and introduces a novel strategy to initialize classifier's parameters, thus preventing biased predictions toward the background class.
- Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation348
This paper introduces a novel approach for monocular depth estimation which is competitive with previous methods on the KITTI benchmark and outperforms the state of the art on the NYU Depth V2 dataset.
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- AutoDIAL: Automatic Domain Alignment Layers337
Opposite to previous works which define a priori in which layers adaptation should be performed, this method is able to automatically learn the degree of feature alignment required at different levels of the deep network.
- A Unified Objective for Novel Class Discovery281
A UNified Objective function (UNO) for discovering novel classes, with the explicit purpose of favoring synergy between supervised and unsupervised learning, and outperforms the state of the art on several benchmarks.
- Transformer-Based Attention Networks for Continuous Pixel-Wise Prediction260
This paper proposes TransDepth, an architecture that benefits from both convolutional neural networks and transformers that applies transformers to pixel-wise prediction problems involving continuous labels and achieves state-of-theart performance on three challenging datasets.
- Self-Supervised Models are Continual Learners246
It is shown that self-supervised loss functions can be seamlessly converted into distillation mechanisms for CL by adding a predictor network that maps the current state of the repre-sentations to their past state.
- Learning Cross-Modal Deep Representations for Robust Pedestrian Detection237
The proposed approach outperforms the state-of-the-art on the challenging KAIST multispectral pedestrian dataset and it is competitive with previous methods on the popular Caltech dataset.
- Neighborhood Contrastive Learning for Novel Class Discovery211
This paper addresses Novel Class Discovery (NCD), the task of unveiling new classes in a set of unlabeled samples given a labeled dataset with known classes, and builds a new framework, named Neighborhood Contrastive Learning (NCL), to learn discriminative representations that are important to clustering performance.
- Unsupervised Adversarial Depth Estimation Using Cycled Generative Networks193
A novel unsupervised deep learning approach for predicting depth maps and showing that the depth estimation task can be effectively tackled within an adversarial learning framework is presented.
- Unsupervised Domain Adaptation Using Feature-Whitening and Consensus Loss182
This work proposes domain alignment layers which implement feature whitening for the purpose of matching source and target feature distributions, and leverage the unlabeled target data by proposing the Min-Entropy Consensus loss, which regularizes training while avoiding the adoption of many user-defined hyper-parameters.
- Precise widening operators for convex polyhedra181
A framework for the systematic definition of new widening operators that are never less precise than a given widening is presented and an improvement to the well-known widening delay technique is suggested that allows one to gain precision while preserving its overall simplicity.
- 3D Object Detection From Images for Autonomous Driving: A Survey180
This paper provides the first comprehensive survey of this novel and continuously growing research field, summarizing the most commonly used pipelines for image-based 3D detection and deeply analyzing each of their components and proposing two new taxonomies to organize the state-of-the-art methods into different categories.
- Best Sources Forward: Domain Generalization through Source-Specific Nets176
This work designs a deep network with multiple domain-specific classifiers, each associated to a source domain, and introduced a domain agnostic component supporting the final classifier.
- SALSA: A Novel Dataset for Multimodal Group Behavior Analysis170
This work proposes SALSA, a novel dataset facilitating multimodal and Synergetic sociAL Scene Analysis, and shows how the recorded multiple cues synergetically aid automatic analysis of social interactions.
- Predicting and Understanding Urban Perception with Convolutional Neural Networks167
A novel approach for predicting the perceived safety of a scene from Google Street View Images using a Convolutional Neural Network (CNN), significantly improving the accuracy of predictions over previous methods.
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- Boosting Domain Adaptation by Discovering Latent Domains161
A novel Convolutional Neural Network (CNN) architecture is introduced which automatically discovers latent domains in visual datasets and exploits this information to learn robust target classifiers and outperforms state-of-the-art multi-source DA methods by a large margin.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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