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Found 16 papers, 4 papers with code
N2F2: Hierarchical Scene Understanding with Nested Neural Feature Fields
To address this, we introduce Nested Neural Feature Fields (N2F2), a novel approach that employs hierarchical supervision to learn a single feature field, wherein different dimensions within the same high-dimensional feature encode scene properties at varying granularities.
SiLVR: Scalable Lidar-Visual Reconstruction with Neural Radiance Fields for Robotic Inspection
We present a neural-field-based large-scale reconstruction system that fuses lidar and vision data to generate high-quality reconstructions that are geometrically accurate and capture photo-realistic textures.
Contrastive Lift: 3D Object Instance Segmentation by Slow-Fast Contrastive Fusion
Our approach outperforms the state-of-the-art on challenging scenes from the ScanNet, Hypersim, and Replica datasets, as well as on our newly created Messy Rooms dataset, demonstrating the effectiveness and scalability of our slow-fast clustering method.
Neural Refinement for Absolute Pose Regression with Feature Synthesis
To enhance the robustness of our model, we introduce a feature fusion module and a progressive training strategy.
A Light Touch Approach to Teaching Transformers Multi-view Geometry
Transformers are powerful visual learners, in large part due to their conspicuous lack of manually-specified priors.
A Prompt Array Keeps the Bias Away: Debiasing Vision-Language Models with Adversarial Learning
Vision-language models can encode societal biases and stereotypes, but there are challenges to measuring and mitigating these multimodal harms due to lacking measurement robustness and feature degradation.
Dynamic Iterative Refinement for Efficient 3D Hand Pose Estimation
While hand pose estimation is a critical component of most interactive extended reality and gesture recognition systems, contemporary approaches are not optimized for computational and memory efficiency.
Data-driven Weight Initialization with Sylvester Solvers
The initialization is cast as an optimization problem where we minimize a combination of encoding and decoding losses of the input activations, which is further constrained by a user-defined latent code.
Structured Convolutions for Efficient Neural Network Design
We show how this decomposition can be applied to 2D and 3D kernels as well as the fully-connected layers.
LSQ+: Improving low-bit quantization through learnable offsets and better initialization
To solve this problem, we propose LSQ+, a natural extension of LSQ, wherein we introduce a general asymmetric quantization scheme with trainable scale and offset parameters that can learn to accommodate the negative activations.
Ranked #18 on
Quantization
on ImageNet
Learned Threshold Pruning
This is in contrast to other methods that search for per-layer thresholds via a computationally intensive iterative pruning and fine-tuning process.
QKD: Quantization-aware Knowledge Distillation
First, Self-studying (SS) phase fine-tunes a quantized low-precision student network without KD to obtain a good initialization.
Teacher-Student Learning Paradigm for Tri-training: An Efficient Method for Unlabeled Data Exploitation
Given that labeled data is expensive to obtain in real-world scenarios, many semi-supervised algorithms have explored the task of exploitation of unlabeled data.
Annotation-cost Minimization for Medical Image Segmentation using Suggestive Mixed Supervision Fully Convolutional Networks
For medical image segmentation, most fully convolutional networks (FCNs) need strong supervision through a large sample of high-quality dense segmentations, which is taxing in terms of costs, time and logistics involved.
FusedLSTM: Fusing frame-level and video-level features for Content-based Video Relevance Prediction
The last section gives a complete comparison of all the approaches implemented during this challenge, including the one presented in the baseline paper.
