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Works164 from public data
- SphereFace: Deep Hypersphere Embedding for Face Recognition3,154
This paper proposes the angular softmax (A-Softmax) loss that enables convolutional neural networks (CNNs) to learn angularly discriminative features in deep face recognition (FR) problem under open-set protocol.
- GR00T N1: An Open Foundation Model for Generalist Humanoid Robots1,558
This work introduces GR00T N1, an open foundation model for humanoid robots that outperforms the state-of-the-art imitation learning baselines on standard simulation benchmarks across multiple robot embodiments and deploys the model on the Fourier GR-1 humanoid robot for language-conditioned bimanual manipulation tasks.
- Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-training1,531
This paper proposes a novel UDA framework based on an iterative self-training (ST) procedure, where the problem is formulated as latent variable loss minimization, and can be solved by alternatively generating pseudo labels on target data and re-training the model with these labels.
- Confidence Regularized Self-Training941
A confidence regularized self-training (CRST) framework, formulated as regularizedSelf-training, that treats pseudo-labels as continuous latent variables jointly optimized via alternating optimization and proposes two types of confidence regularization: label regularization (LR) and modelRegularization (MR).
- 927
- Joint Discriminative and Generative Learning for Person Re-Identification856
This paper proposes a joint learning framework that couples re-id learning and data generation end-to-end and renders significant improvement over the baseline without using generated data, leading to the state-of-the-art performance on several benchmark datasets.
- Image based Static Facial Expression Recognition with Multiple Deep Network Learning605
This work reports the proposed image based static facial expression recognition method for the Emotion Recognition in the Wild Challenge (EmotiW) 2015, and presents two schemes for learning the ensemble weights of the network responses by minimizing the log likelihood loss and the hinge loss.
- 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.
- 408
- CASENet: Deep Category-Aware Semantic Edge Detection300
This work proposes a novel end-to-end deep semantic edge learning architecture based on ResNet and a new skip-layer architecture where category-wise edge activations at the top convolution layer share and are fused with the same set of bottom layer features.
- 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.
- Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation287
This paper proposes uncertainty-aware multi-view co-training (UMCT), a unified framework that addresses these two tasks for volumetric medical image segmentation and can even effectively handle the challenging situation where labeled source data is inaccessible.
- 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.
- Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering257
A novel method to eliminate the effects of the errors from the projection space (representation) rather than from the input space is presented and a method to construct a sparse similarity graph, called L2-graph is introduced.
- 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.
- Joint Disentangling and Adaptation for Cross-Domain Person Re-Identification244
This paper proposes a joint learning framework that disentangles id-related/unrelated features and enforces adaptation to work on the id- related feature space exclusively, and outperforms the state-of-the-art methods by clear margins.
- Instance-Aware, Context-Focused, and Memory-Efficient Weakly Supervised Object Detection231
This work develops an instance-aware and context-focused unified framework for weakly supervised video object detection that achieves state-of-the-art results on COCO, VOC 2007, and VOC 2012 while devising a memory-efficient sequential batch back-propagation.
- 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.
- 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.
- Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders164
This study provides an extensive exploration of the design space for MLLMs using a mixture of vision encoders and resolutions, finding that simply concatenating visual tokens from a set of complementary vision encoders is as effective as more complex mixing architectures or strategies.
- Delving Deeper into Anti-Aliasing in ConvNets160
An adaptive content-aware low-pass filtering layer, which predicts separate filter weights for each spatial location and channel group of the input feature maps, which effectively adapts to the different feature frequencies to avoid aliasing while preserving useful information for recognition.
- FreeSOLO: Learning to Segment Objects without Annotations155
This work proposes a fully unsupervised learning method that learns class-agnostic instance segmentation without any annotations, and presents a novel localization-aware pre-training framework, where objects can be discovered from complicated scenes in an unsuper supervised manner.
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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