Search Results for author: Hisham Cholakkal

Found 16 papers, 7 papers with code

Fixing Localization Errors to Improve Image Classification

1 code implementation ECCV 2020 Guolei Sun, Salman Khan, Wen Li, Hisham Cholakkal, Fahad Shahbaz Khan, Luc van Gool

This way, in an effort to fix localization errors, our loss provides an extra supervisory signal that helps the model to better discriminate between similar classes.

Classification General Classification +3

Count- and Similarity-aware R-CNN for Pedestrian Detection

no code implementations ECCV 2020 Jin Xie, Hisham Cholakkal, Rao Muhammad Anwer, Fahad Shahbaz Khan, Yanwei Pang, Ling Shao, Mubarak Shah

We further introduce a count-and-similarity branch within the two-stage detection framework, which predicts pedestrian count as well as proposal similarity.

Human Instance Segmentation Pedestrian Detection +1

Structured Latent Embeddings for Recognizing Unseen Classes in Unseen Domains

no code implementations12 Jul 2021 Shivam Chandhok, Sanath Narayan, Hisham Cholakkal, Rao Muhammad Anwer, Vineeth N Balasubramanian, Fahad Shahbaz Khan, Ling Shao

The need to address the scarcity of task-specific annotated data has resulted in concerted efforts in recent years for specific settings such as zero-shot learning (ZSL) and domain generalization (DG), to separately address the issues of semantic shift and domain shift, respectively.

Domain Generalization Zero-Shot Learning

Handwriting Transformers

1 code implementation8 Apr 2021 Ankan Kumar Bhunia, Salman Khan, Hisham Cholakkal, Rao Muhammad Anwer, Fahad Shahbaz Khan, Mubarak Shah

We propose a novel transformer-based styled handwritten text image generation approach, HWT, that strives to learn both style-content entanglement as well as global and local writing style patterns.

Image Generation Text Generation

SipMask: Spatial Information Preservation for Fast Image and Video Instance Segmentation

1 code implementation ECCV 2020 Jiale Cao, Rao Muhammad Anwer, Hisham Cholakkal, Fahad Shahbaz Khan, Yanwei Pang, Ling Shao

In terms of real-time capabilities, SipMask outperforms YOLACT with an absolute gain of 3. 0% (mask AP) under similar settings, while operating at comparable speed on a Titan Xp.

Object Detection Real-time Instance Segmentation +2

PSC-Net: Learning Part Spatial Co-occurrence for Occluded Pedestrian Detection

no code implementations25 Jan 2020 Jin Xie, Yanwei Pang, Hisham Cholakkal, Rao Muhammad Anwer, Fahad Shahbaz Khan, Ling Shao

On the heavy occluded (\textbf{HO}) set of CityPerosns test set, our PSC-Net obtains an absolute gain of 4. 0\% in terms of log-average miss rate over the state-of-the-art with same backbone, input scale and without using additional VBB supervision.

Pedestrian Detection

Fine-grained Recognition: Accounting for Subtle Differences between Similar Classes

no code implementations14 Dec 2019 Guolei Sun, Hisham Cholakkal, Salman Khan, Fahad Shahbaz Khan, Ling Shao

The main requisite for fine-grained recognition task is to focus on subtle discriminative details that make the subordinate classes different from each other.

Fine-Grained Image Classification

Towards Partial Supervision for Generic Object Counting in Natural Scenes

1 code implementation13 Dec 2019 Hisham Cholakkal, Guolei Sun, Salman Khan, Fahad Shahbaz Khan, Ling Shao, Luc van Gool

Our RLC framework further reduces the annotation cost arising from large numbers of object categories in a dataset by only using lower-count supervision for a subset of categories and class-labels for the remaining ones.

Image Classification Instance Segmentation +2

3C-Net: Category Count and Center Loss for Weakly-Supervised Action Localization

1 code implementation ICCV 2019 Sanath Narayan, Hisham Cholakkal, Fahad Shahbaz Khan, Ling Shao

Our joint formulation has three terms: a classification term to ensure the separability of learned action features, an adapted multi-label center loss term to enhance the action feature discriminability and a counting loss term to delineate adjacent action sequences, leading to improved localization.

Action Classification Weakly Supervised Action Localization +2

Object Counting and Instance Segmentation with Image-level Supervision

2 code implementations CVPR 2019 Hisham Cholakkal, Guolei Sun, Fahad Shahbaz Khan, Ling Shao

Moreover, our approach improves state-of-the-art image-level supervised instance segmentation with a relative gain of 17. 8% in terms of average best overlap, on the PASCAL VOC 2012 dataset.

Instance Segmentation Object Counting +1

L1-regularized Reconstruction Error as Alpha Matte

no code implementations9 Feb 2017 Jubin Johnson, Hisham Cholakkal, Deepu Rajan

Sampling-based alpha matting methods have traditionally followed the compositing equation to estimate the alpha value at a pixel from a pair of foreground (F) and background (B) samples.

Video Matting

Backtracking Spatial Pyramid Pooling (SPP)-based Image Classifier for Weakly Supervised Top-down Salient Object Detection

no code implementations16 Nov 2016 Hisham Cholakkal, Jubin Johnson, Deepu Rajan

First, the probabilistic contribution of each image region to the confidence of a CNN-based image classifier is computed through a backtracking strategy to produce top-down saliency.

RGB Salient Object Detection Salient Object Detection

Backtracking ScSPM Image Classifier for Weakly Supervised Top-Down Saliency

no code implementations CVPR 2016 Hisham Cholakkal, Jubin Johnson, Deepu Rajan

We propose a weakly supervised top-down saliency framework using only binary labels that indicate the presence/absence of an object in an image.

Object Detection

A Classifier-guided Approach for Top-down Salient Object Detection

no code implementations22 Apr 2016 Hisham Cholakkal, Jubin Johnson, Deepu Rajan

Although the role of the classifier is to support salient object detection, we evaluate its performance in image classification and also illustrate the utility of thresholded saliency maps for image segmentation.

Classification General Classification +4

Sparse Coding for Alpha Matting

no code implementations11 Apr 2016 Jubin Johnson, Ehsan Shahrian Varnousfaderani, Hisham Cholakkal, Deepu Rajan

In this paper, the matting problem is reinterpreted as a sparse coding of pixel features, wherein the sum of the codes gives the estimate of the alpha matte from a set of unpaired F and B samples.

Image Matting Video Matting

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