Author name cluster

Shafin Rahman

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

1 author row

TMLR Journal 2025 Journal Article

LumiNet: Perception-Driven Knowledge Distillation via Statistical Logit Calibration

Md. Ismail Hossain
M M Lutfe Elahi
Sameera Ramasinghe
Ali Cheraghian
Fuad Rahman
Nabeel Mohammed
Shafin Rahman

In the knowledge distillation literature, feature-based methods have dominated due to their ability to effectively tap into extensive teacher models. In contrast, logit-based approaches, which aim to distill `dark knowledge' from teachers, typically exhibit inferior performance compared to feature-based methods. To bridge this gap, we present LumiNet, a novel knowledge distillation algorithm designed to enhance logit-based distillation. We introduce the concept of `perception', aiming to calibrate logits based on the model's representation capability. This concept addresses overconfidence issues in the logit-based distillation method while also introducing a novel method to distill knowledge from the teacher. It reconstructs the logits of a sample/instances by considering relationships with other samples in the batch. LumiNet excels on benchmarks like CIFAR-100, ImageNet, and MSCOCO, outperforming the leading feature-based methods, e.g., compared to KD with ResNet18 and MobileNetV2 on ImageNet, it shows improvements of 1.5\% and 2.05\%, respectively.

PDF Details

AAAI Conference 2024 Conference Paper

SEA-GWNN: Simple and Effective Adaptive Graph Wavelet Neural Network

Swakshar Deb
Sejuti Rahman
Shafin Rahman

The utilization of wavelet-based techniques in graph neural networks (GNNs) has gained considerable attention, particularly in the context of node classification. Although existing wavelet-based approaches have shown promise, they are constrained by their reliance on pre-defined wavelet filters, rendering them incapable of effectively adapting to signals that reside on graphs based on tasks at hand. Recent research endeavors address this issue through the introduction of a wavelet lifting transform. However, this technique necessitates the use of bipartite graphs, causing a transformation of the original graph structure into a bipartite configuration. This alteration of graph topology results in the generation of undesirable wavelet filters, thereby undermining the effectiveness of the method. In response to these challenges, we propose a novel simple and effective adaptive graph wavelet neural network (SEA-GWNN) class that employs the lifting scheme on arbitrary graph structures while upholding the original graph topology by leveraging multi-hop computation trees. A noteworthy aspect of the approach is the focus on local substructures represented as acyclic trees, wherein the lifting strategy is applied in a localized manner. This locally defined lifting scheme effectively combines high-pass and low-pass frequency information to enhance node representations. Furthermore, to reduce computing costs, we propose to decouple the higher- order lifting operators and induce them from the lower-order structures. Finally, we benchmark our model on several real- world datasets spanning four distinct categories, including citation networks, webpages, the film industry, and large-scale graphs and the experimental results showcase the efficacy of the proposed SEA-GWNN.

PDF Details DOI

AAAI Conference 2020 Conference Paper

Improved Visual-Semantic Alignment for Zero-Shot Object Detection

Shafin Rahman
Salman Khan
Nick Barnes

Zero-shot object detection is an emerging research topic that aims to recognize and localize previously ‘unseen’ objects. This setting gives rise to several unique challenges, e. g. , highly imbalanced positive vs. negative instance ratio, proper alignment between visual and semantic concepts and the ambiguity between background and unseen classes. Here, we propose an end-to-end deep learning framework underpinned by a novel loss function that handles class-imbalance and seeks to properly align the visual and semantic cues for improved zero-shot learning. We call our objective the ‘Polarity loss’ because it explicitly maximizes the gap between positive and negative predictions. Such a margin maximizing formulation is not only important for visual-semantic alignment but it also resolves the ambiguity between background and unseen objects. Further, the semantic representations of objects are noisy, thus complicating the alignment between visual and semantic domains. To this end, we perform metric learning using a ‘Semantic vocabulary’ of related concepts that reﬁnes the noisy semantic embeddings and establishes a better synergy between visual and semantic domains. Our approach is inspired by the embodiment theories in cognitive science, that claim human semantic understanding to be grounded in past experiences (seen objects), related linguistic concepts (word vocabulary) and the visual perception (seen/unseen object images). Our extensive results on MS-COCO and Pascal VOC datasets show signiﬁcant improvements over state of the art. 1

PDF Details

NeurIPS Conference 2015 Conference Paper

Saliency, Scale and Information: Towards a Unifying Theory

Shafin Rahman
Neil Bruce

In this paper we present a definition for visual saliency grounded in information theory. This proposal is shown to relate to a variety of classic research contributions in scale-space theory, interest point detection, bilateral filtering, and to existing models of visual saliency. Based on the proposed definition of visual saliency, we demonstrate results competitive with the state-of-the art for both prediction of human fixations, and segmentation of salient objects. We also characterize different properties of this model including robustness to image transformations, and extension to a wide range of other data types with 3D mesh models serving as an example. Finally, we relate this proposal more generally to the role of saliency computation in visual information processing and draw connections to putative mechanisms for saliency computation in human vision.

PDF Details