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Data-free knowledge distillation (DFKD) improves the student model (S) by mimicking the class probability from a pre-trained teacher model (T) without training data. Under such setting, an ideal scenario is that T can help generate ”good” ...

The proliferation of AI-powered search and recommendation systems has accelerated the formation of “filter bubbles” that reinforce people’s biases and narrow their perspectives. Previous research has attempted to address this issue by increasing the ...

Partial label learning (PLL) induces a multi-class classifier from training examples each associated with a set of candidate labels, among which only one is valid. The formation of real-world data typically arises from heterogeneous entanglement of ...

Predicting the unknown from the first-person perspective is expected as a necessary step toward machine intelligence, which is essential for practical applications including autonomous driving and robotics. As a human-level task, egocentric action ...

Domain adaptation (DA) addresses the challenge of distribution discrepancy between the training and test data, while multi-source domain adaptation (MSDA) is particularly appealing for realistic scenarios. With the emergence of extensive unlabeled ...

Recently, visual food analysis has received more and more attention in the computer vision community due to its wide application scenarios, e.g., diet nutrition management, smart restaurant, and personalized diet recommendation. Considering that food ...

Since the advent of knowledge distillation, many researchers have been intrigued by the dark knowledge hidden in the soft labels generated by the teacher model. This prompts us to scrutinize the circumstances under which these soft labels are effective. ...

Multi-label classification aims to recognize multiple objects or attributes from images. The key to solving this issue relies on effectively characterizing the inter-label correlations or dependencies, which bring the prevailing graph neural network. ...

Data imbalance is an essential issue in multi-label image classification that may reduce the model’s generalization ability. Unlike oversampling or undersampling in single-label image classification, simply removing or repeating some images in ...

Video moment localization, also known as video moment retrieval, aims to search a target segment within a video described by a given natural language query. Beyond the task of temporal action localization whereby the target actions are pre-defined, video ...

Image inpainting has achieved remarkable progress and inspired abundant methods, where the critical bottleneck is identified as how to fulfill the high-frequency structure and low-frequency texture information on the masked regions with semantics. To ...

Domain adaptation (DA) tries to tackle the scenarios when the test data does not fully follow the same distribution of the training data, and multi-source domain adaptation (MSDA) is very attractive for real world applications. By learning from large-...

The click feature of an image, defined as the user click frequency vector of the image on a predefined word vocabulary, is known to effectively reduce the semantic gap for fine-grained image recognition. Unfortunately, user click frequency data are ...

Understanding user preference on item context is the key to acquire a high-quality multimedia recommendation. Typically, the pre-existing features of items are derived from pre-trained models (e.g. visual features of micro-videos extracted from some ...

In this paper, we present HoloBoard, an interactive large-format pseduo-holographic display system for lecture based classes. With its unique properties of immersive visual display and transparent screen, we designed and implemented a rich set of novel ...

Pre-training has been an emerging topic that provides a way to learn strong representation in many fields (e.g., natural language processing, computing vision). In the last few years, we have witnessed many research works on multi-modal pre-training ...

It is our great pleasure to welcome you to the ICMR 2021 Workshop on Multi-Modal Pre- Training for Multimedia Understanding - MMPT 2021.

The First International Joint Workshop on Multi-Modal Pre-Training for Multimedia Understanding aims to gather peer ...

In this paper, we focus on egocentric action anticipation from videos, which enables various applications, such as helping intelligent wearable assistants understand users' needs and enhance their capabilities in the interaction process. It requires ...

As an important computer vision task, image matching requires efficient and discriminative local descriptors. Most of the existing descriptors like SIFT and ORB are hand-crafted; therefore it is necessary to study more optimized descriptors through end-to-...

The click feature of an image, defined as a user click count vector based on click data, has been demonstrated to be effective for reducing the semantic gap for image recognition. Unfortunately, most of the traditional image recognition datasets do not ...