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- PieAPP: Perceptual Image-Error Assessment Through Pairwise Preference396
A new learning-based method that is the first to predict perceptual image error like human observers, and significantly outperforms existing algorithms, beating the state-of-the-art by almost 3× on the authors' test set in terms of binary error rate, while also generalizing to new kinds of distortions, unlike previous learning- based methods.
- PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language Models168
This paper explores an alternative way for low-shot part segmentation of 3D point clouds by leveraging a pretrained image-language model, GLIP, which achieves superior performance on open-vocabulary 2D detection and a novel 2D-to-3D label lifting algorithm.
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- XModal-ID: Using WiFi for Through-Wall Person Identification from Candidate Video Footage101
Results show that XModal-ID can robustly identify new people walking in new environments, in various practical scenarios.
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- X-Align++: cross-modal cross-view alignment for Bird’s-eye-view segmentation55
This paper proposes X-Align, a novel end-to-end cross-modal and cross-view learning framework for BEV segmentation consisting of a novel Cross-Modal Feature Alignment (X-FA) loss, and provides extensive ablation studies to demonstrate the effectiveness of the individual components.
- AuxAdapt: Stable and Efficient Test-Time Adaptation for Temporally Consistent Video Semantic Segmentation50
An efficient, intuitive, and unsupervised online adaptation method, AuxAdapt, for improving the temporal consistency of most neural network models, which does not require optical flow and only takes one pass of the video.
- Teaching RF to Sense without RF Training Measurements48
This paper proposes a novel, generalizable, and scalable idea that eliminates the need for collecting Radio Frequency (RF) measurements, when training RF sensing systems for human-motion-related activities, and can successfully train an RF sensing system without any real RF measurements.
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- MAMo: Leveraging Memory and Attention for Monocular Video Depth Estimation33
MAMo is proposed, a novel memory and attention framework for monocular video depth estimation that consistently improves monocular depth estimation networks and sets new state-of-the-art (SOTA) accuracy.
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- Factorized Inverse Path Tracing for Efficient and Accurate Material-Lighting Estimation32
The Factorized Inverse Path Tracing (FIPT) addresses challenges by using a factored light transport formulation and finds emitters driven by rendering errors, and enables accurate material and lighting optimization faster than previous work, and is more effective at resolving ambiguities.
- EGA-Depth: Efficient Guided Attention for Self-Supervised Multi-Camera Depth Estimation27
A novel guided attention architecture, EGA-Depth, which can improve both the efficiency and accuracy of self-supervised multi-camera depth estimation by using its perspective view as the query to cross-reference its neighboring views to derive informative features for this camera view.
- OCAI: Improving Optical Flow Estimation by Occlusion and Consistency Aware Interpolation24
This work proposes OCAI, a method that supports robust frame interpolation by generating intermediate video frames along-side optical flows in between and introduces a teacher-student style semi-supervised learning method on top of the interpolated frames.
- X-Distill: Improving Self-Supervised Monocular Depth via Cross-Task Distillation24
A novel method to improve the self-supervised training of monocular depth via cross-task knowledge distillation from semantic segmentation to depth estimation, which significantly improves the depth estimation accuracy and outperforms the state of the art.
- Motion and communication co-optimization with path planning and online channel prediction24
An in-house algorithm is utilized that computes near-optimal solutions in little time and enables its use in the online setting, and the optimization strategy is described in detail and validated by simulation of realistic scenarios.
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- DistractFlow: Improving Optical Flow Estimation via Realistic Distractions and Pseudo-Labeling21
A novel data augmentation approach for training optical flow estimation models by introducing realistic distractions to the input frames by combining one of the frames in the pair with a distractor image depicting a similar domain, which allows for inducing visual perturbations congruent with natural objects and scenes.
- 4D Panoptic Segmentation as Invariant and Equivariant Field Prediction21
This paper develops rotation-equivariant neural networks for 4D panoptic segmentation and sets the new state-of-the-art performance and achieves 1st place on the SemanticKITTI 4D Panoptic Segmentation leaderboard.
- HS3: Learning with Proper Task Complexity in Hierarchically Supervised Semantic Segmentation20
This paper proposes Hierarchically Supervised Semantic Segmentation (HS3), a training scheme that supervises intermediate layers in a segmentation network to learn meaningful representations by varying task complexity, and devise a fusion framework, HS3-Fuse, to aggregate the hierarchical features generated by these layers.
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- Panoptic, Instance and Semantic Relations: A Relational Context Encoder to Enhance Panoptic Segmentation19
This paper presents a novel framework to integrate both semantic and instance contexts for panoptic segmentation, and proposes a novel Panoptic, Instance, and Semantic Relations (PISR) module to exploit such contexts.
- Perceptual Consistency in Video Segmentation19
A novel perceptual consistency perspective on video semantic segmentation is presented, which can capture both temporal consistency and pixel-wise correctness and be used as a regularizer during the training of segmentation models, which leads to more temporally consistent video segmentation while maintaining accuracy.
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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 2026-10-14. 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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