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Works185 from public data
- ERFNet: Efficient Residual Factorized ConvNet for Real-Time Semantic Segmentation1,484
A deep architecture that is able to run in real time while providing accurate semantic segmentation, and a novel layer that uses residual connections and factorized convolutions in order to remain efficient while retaining remarkable accuracy is proposed.
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- Dreaming to Distill: Data-Free Knowledge Transfer via DeepInversion738
DeepInversion is introduced, a new method for synthesizing images from the image distribution used to train a deep neural network, which optimizes the input while regularizing the distribution of intermediate feature maps using information stored in the batch normalization layers of the teacher.
- See through Gradients: Image Batch Recovery via GradInversion648
It is shown that gradients encode a surprisingly large amount of information, such that all the individual images can be recovered with high fidelity via GradInversion, even for complex datasets, deep networks, and large batch sizes.
- A-ViT: Adaptive Tokens for Efficient Vision Transformer606
This work introduces A - ViT, a method that adaptively adjusts the inference cost of vision transformer (ViT) for images of different complexity by automatically reducing the number of tokens in vision transformers that are processed in the network as inference proceeds, and demonstrates that it requires no extra parameters or sub-network for halting.
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- VoxFormer: Sparse Voxel Transformer for Camera-Based 3D Semantic Scene Completion479
VoxFormer, a Transformer-based semantic scene completion framework that can output complete 3D volumetric semantics from only 2D images that outperforms the state of the art with a relative improvement of 20.0% in geometry and 18.1% in semantics.
- Cost Volume Pyramid Based Depth Inference for Multi-View Stereo429
It is demonstrated that building a cost volume pyramid in a coarse-to-fine manner instead of constructing a costvolume at a fixed resolution leads to a compact, lightweight network and allows us inferring high resolution depth maps to achieve better reconstruction results.
- Less Is More: Towards Compact CNNs343
This work shows that, by incorporating sparse constraints into the objective function, it is possible to decimate the number of neurons during the training stage, thus theNumber of parameters and the memory footprint of the neural network are reduced, which is desirable at the test time.
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- Is Ego Status All You Need for Open-Loop End-to-End Autonomous Driving?296
This paper conducts thorough analyses and introduces a new metric to evaluate whether the predicted trajectories adhere to the road, and proposes a simple baseline able to achieve competitive results without relying on perception annotations.
- Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation288
Hydra-MDP, a novel paradigm employing multiple teachers in a teacher-student model, which features a multi-head decoder to learn diverse trajectory candidates tailored to various evaluation metrics, achieves the 1-st place in the Navsim challenge.
- Understanding The Robustness in Vision Transformers268
This paper examines the role of self-attention in learning robust representations in Vision Transformers and proposes a family of fully attentional networks (FANs) that strengthen this capability by incorporating an attentional channel processing design.
- Context Based Emotion Recognition using EMOTIC Dataset266
This paper presents EMOTIC, a dataset of images of people in a diverse set of natural situations, annotated with their apparent emotion, and trains different CNN models for emotion recognition, combining the information of the bounding box containing the person with the contextual information extracted from the scene.
- Road Scene Segmentation from a Single Image261
It is concluded that convolutional neural networks are suitable for learning 3D scene layout from noisy labels and provides a relative improvement of 7% compared to the baseline and combining color planes provides a statistical description of road areas that exhibits maximal uniformity.
- M$^2$BEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Birds-Eye View Representation249
A unified framework that jointly performs 3D object detection and map segmentation in the Birds Eye View~(BEV) space with multi-camera image inputs, which is memory efficient, allowing significantly higher resolution images as input, with faster inference speed.
- FB-OCC: 3D Occupancy Prediction based on Forward-Backward View Transformation222
The proposed solution FB-OCC builds upon FB-BEV, a cutting-edge camera-based bird's-eye view perception design using forward-backward projection, and results in a state-of-the-art mIoU score of 54.19% on the nuScenes dataset.
- Learning Image Matching by Simply Watching Video217
An unsupervised learning based approach to the ubiquitous computer vision problem of image matching that achieves surprising performance comparable to traditional empirically designed methods.
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- OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reasoning212
This work proposes OmniDrive, a holistic vision-language dataset that aligns agent models with 3D driving tasks through counter-factual reasoning, and explores two advanced OmniDrive-Agent frameworks to assess the importance of vision-language alignment versus 3D perception.
- Panoptic SegFormer: Delving Deeper into Panoptic Segmentation with Transformers199
This work presents Panoptic SegFormer, a general framework for panoptic segmentation with transformers, which contains three innovative components: an efficient deeply-supervised mask decoder, a query decoupling strategy, and an improved postprocessing method.
- FB-BEV: BEV Representation from Forward-Backward View Transformations171
This work proposes a novel forward-backward view transformation module that compensates for the deficiencies in both existing methods, allowing them to enhance each other to obtain higher quality BEV representations mutually.
- Effective Use of Synthetic Data for Urban Scene Semantic Segmentation164
A drastically different way to handle synthetic images that does not require seeing any real images at training time is introduced, which builds on the observation that foreground and background classes are not affected in the same manner by the domain shift, and thus should be treated differently.
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- FasterViT: Fast Vision Transformers with Hierarchical Attention158
The newly introduced Hierarchical Attention (HAT) approach decomposes global self-attention with quadratic complexity into a multi-level attention with reduced computational costs and can be used as a plug-and-play module for existing networks and enhance them.
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 2026-10-16. 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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