Search Results for author:
Found 12 papers, 1 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).
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.
Raising the Bar on the Evaluation of Out-of-Distribution Detection
In this paper, we define 2 categories of OoD data using the subtly different concepts of perceptual/visual and semantic similarity to in-distribution (iD) data.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
+2
Fast Adaptive Anomaly Detection
Anything outside of a given normal population is by definition an anomaly.
Visualizing and Describing Fine-grained Categories as Textures
We analyze how categories from recent FGVC challenges can be described by their textural content.
Second-order Democratic Aggregation
In this paper, we study a class of orderless aggregation functions designed to minimize interference or equalize contributions in the context of second-order features and we show that they can be computed just as efficiently as their first-order counterparts and they have favorable properties over aggregation by summation.
Improved Bilinear Pooling with CNNs
We present an alternative scheme for computing gradients that is faster and yet it offers improvements over the baseline model.
Implicit Sparse Code Hashing
We address the problem of converting large-scale high-dimensional image data into binary codes so that approximate nearest-neighbor search over them can be efficiently performed.
Bilinear CNN Models for Fine-Grained Visual Recognition
We propose bilinear models, a recognition architecture that consists of two feature extractors whose outputs are multiplied using outer product at each location of the image and pooled to obtain an image descriptor.
Ranked #62 on
Fine-Grained Image Classification
on CUB-200-2011
Fine-Grained Image Classification
Fine-Grained Visual Recognition
Visualizing and Understanding Deep Texture Representations
A number of recent approaches have used deep convolutional neural networks (CNNs) to build texture representations.
One-to-many face recognition with bilinear CNNs
We demonstrate the performance of the B-CNN model beginning from an AlexNet-style network pre-trained on ImageNet.
Bilinear CNNs for Fine-grained Visual Recognition
We then present a systematic analysis of these networks and show that (1) the bilinear features are highly redundant and can be reduced by an order of magnitude in size without significant loss in accuracy, (2) are also effective for other image classification tasks such as texture and scene recognition, and (3) can be trained from scratch on the ImageNet dataset offering consistent improvements over the baseline architecture.
Ranked #23 on
Fine-Grained Image Classification
on NABirds
Fine-Grained Image Classification
Fine-Grained Visual Recognition
+1
