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Works125 from public data
- Deep Back-Projection Networks for Super-Resolution1,599
It is shown that extending the idea to allow concatenation of features across up- and downsampling stages (Dense DBPN) allows us to reconstruct further improve super-resolution, yielding superior results and in particular establishing new state of the art results for large scaling factors such as 8× across multiple data sets.
- FractalNet: Ultra-Deep Neural Networks without Residuals.1,078
In experiments, fractal networks match the excellent performance of standard residual networks on both CIFAR and ImageNet classification tasks, thereby demonstrating that residual representations may not be fundamental to the success of extremely deep convolutional neural networks.
- Learning Representations for Automatic Colorization1,070
A fully automatic image colorization system that leverages recent advances in deep networks, exploiting both low-level and semantic representations, and explores colorization as a vehicle for self-supervised visual representation learning.
- Score Jacobian Chaining: Lifting Pretrained 2D Diffusion Models for 3D Generation709
This work proposes to apply chain rule on the learned gradients, and back-propagate the score of a diffusion model through the Jacobian of a differentiable renderer, which is instantiate to be a voxel radiance field.
- Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)592
This volume presents theoretical and practical discussions of nearest-neighbor (NN) methods in machine learning and examines computer vision as an application domain in which the benefit of these advanced methods is often dramatic.
- Nearest-neighbor methods in learning and vision : theory and practice592
This volume presents theoretical and practical discussions of nearest-neighbor (NN) methods in machine learning and examines computer vision as an application domain in which the benefit of these advanced methods is often dramatic.
- Recurrent Back-Projection Network for Video Super-Resolution553
A novel architecture for the problem of video super-resolution that integrates spatial and temporal contexts from continuous video frames using a recurrent encoder-decoder module, that fuses multi-frame information with the more traditional, single frame super- resolution path for the target frame.
- Colorization as a Proxy Task for Visual Understanding524
This work investigates and improves self-supervision as a drop-in replacement for ImageNet pretraining, focusing on automatic colorization as the proxy task, and presents the first in-depth analysis of self- supervision via colorization, concluding that formulation of the loss, training details and network architecture play important roles in its effectiveness.
- Feedforward semantic segmentation with zoom-out features484
This work introduces a purely feed-forward architecture for semantic segmentation that exploits statistical structure in the image and in the label space without setting up explicit structured prediction mechanisms, and thus avoids complex and expensive inference.
- Instant3D: Fast Text-to-3D with Sparse-View Generation and Large Reconstruction Model456
This paper proposes Instant3D, a novel method that generates high-quality and diverse 3D assets from text prompts in a feed-forward manner and can generate diverse3D assets of high visual quality within 20 seconds, which is two orders of magnitude faster than previous optimization-based methods.
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- Style Transfer by Relaxed Optimal Transport and Self-Similarity331
The results indicate that for any desired level of content preservation, the proposed Style Transfer by Relaxed Optimal Transport and Self-Similarity (STROTSS), a new optimization-based style transfer algorithm, provides higher quality stylization than prior work.
- Face Recognition from Long-Term Observations315
This work addresses the problem of face recognition from a large set of images obtained over time - a task arising in many surveillance and authentication applications and proposes an information-theoretic algorithm that classifies sets of images using the relative entropy between the estimated density of the input set and that of stored collections of images for each class.
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- Decoding Complete Reach and Grasp Actions from Local Primary Motor Cortex Populations278
The results suggest that achieving high-dimensional reach and grasp actions with neuroprosthetic devices may be possible using small intracortical arrays like those already being tested in human pilot clinical trials.
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- Nearest-Neighbor Searching and Metric Space Dimensions247
Several measures of dimension can be estimated using nearest-neighbor searching, while others can be used to estimate the cost of that searching.
- Task-Driven Super Resolution: Object Detection in Low-Resolution Images226
A novel framework to train a deep neural network where the SR sub-network explicitly incorporates a detection loss in its training objective, via a tradeoff with a traditional detection loss is proposed.
- Discriminability Objective for Training Descriptive Captions213
By incorporating into the captioning training objective a loss component directly related to ability (by a machine) to disambiguate image/caption matches, this work obtains systems that produce much more discriminative caption, according to human evaluation.
- Face Recognition in Subspaces209
This chapter describes in roughly chronologic order techniques that identify, parameterize, and analyze linear and nonlinear subspaces, from the original Eigenfaces technique to the recently introduced Bayesian method for probabilistic similarity analysis.
- Diverse M-Best Solutions in Markov Random Fields200
This paper proposes an algorithm for the Diverse M-Best problem, which involves finding a diverse set of highly probable solutions under a discrete probabilistic model and shows that for certain families of dissimilarity functions the authors can guarantee that these solutions can be found as easily as the MAP solution.
- Comprehension-Guided Referring Expressions187
A comprehension module trained on human-generated expressions serves as a differentiable proxy of human evaluation, providing training signal to the generation module, and it is shown that both approaches lead to improved referring expression generation on multiple benchmark datasets.
- Learning task-specific similarity159
An algorithmic approach to learning similarity from examples of what objects are deemed similar according to the task-specific notion of similarity at hand, as well as optional negative examples, which allows to predict when two previously unseen examples are similar and to efficiently search a very large database for examples similar to a query.
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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