Search Results for author:
Found 16 papers, 4 papers with code
Learning to Localize Objects Improves Spatial Reasoning in Visual-LLMs
Integration of Large Language Models (LLMs) into visual domain tasks, resulting in visual-LLMs (V-LLMs), has enabled exceptional performance in vision-language tasks, particularly for visual question answering (VQA).
Universal Pyramid Adversarial Training for Improved ViT Performance
Recently, Pyramid Adversarial training (Herrmann et al., 2022) has been shown to be very effective for improving clean accuracy and distribution-shift robustness of vision transformers.
Revisiting Kernel Temporal Segmentation as an Adaptive Tokenizer for Long-form Video Understanding
While most modern video understanding models operate on short-range clips, real-world videos are often several minutes long with semantically consistent segments of variable length.
Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
Modern hierarchical vision transformers have added several vision-specific components in the pursuit of supervised classification performance.
Ranked #1 on
Image Classification
on iNaturalist 2019
(using extra training data)
Open Vocabulary Semantic Segmentation with Patch Aligned Contrastive Learning
We introduce Patch Aligned Contrastive Learning (PACL), a modified compatibility function for CLIP's contrastive loss, intending to train an alignment between the patch tokens of the vision encoder and the CLS token of the text encoder.
Unifying Tracking and Image-Video Object Detection
We propose TrIVD (Tracking and Image-Video Detection), the first framework that unifies image OD, video OD, and MOT within one end-to-end model.
Robustness and Generalization via Generative Adversarial Training
Adversarial training with these examples enable the model to withstand a wide range of attacks by observing a variety of input alterations during training.
Augmentation-Interpolative AutoEncoders for Unsupervised Few-Shot Image Generation
We aim to build image generation models that generalize to new domains from few examples.
Self-supervised Learning of Point Clouds via Orientation Estimation
A point cloud can be rotated in infinitely many ways, which provides a rich label-free source for self-supervision.
Ranked #10 on
3D Point Cloud Linear Classification
on ModelNet40
3D Point Cloud Linear Classification
Self-Supervised Learning
+1
Coupling Explicit and Implicit Surface Representations for Generative 3D Modeling
We propose a novel neural architecture for representing 3D surfaces, which harnesses two complementary shape representations: (i) an explicit representation via an atlas, i. e., embeddings of 2D domains into 3D; (ii) an implicit-function representation, i. e., a scalar function over the 3D volume, with its levels denoting surfaces.
Fine-grained Synthesis of Unrestricted Adversarial Examples
We propose a novel approach for generating unrestricted adversarial examples by manipulating fine-grained aspects of image generation.
Neural Puppet: Generative Layered Cartoon Characters
We capture these subtle changes by applying an image translation network to refine the mesh rendering, providing an end-to-end model to generate new animations of a character with high visual quality.
Deep Fundamental Matrix Estimation without Correspondences
Estimating fundamental matrices is a classic problem in computer vision.
Generative Adversarial Perturbations
In this paper, we propose novel generative models for creating adversarial examples, slightly perturbed images resembling natural images but maliciously crafted to fool pre-trained models.
Vision-based Real Estate Price Estimation
Using deep convolutional neural networks on a large dataset of photos of home interiors and exteriors, we develop a method for estimating the luxury level of real estate photos.
Stacked Generative Adversarial Networks
In this paper, we propose a novel generative model named Stacked Generative Adversarial Networks (SGAN), which is trained to invert the hierarchical representations of a bottom-up discriminative network.
Ranked #11 on
Conditional Image Generation
on CIFAR-10
(Inception score metric)
