34 research outputs found
Temporal Cross-Media Retrieval with Soft-Smoothing
Multimedia information have strong temporal correlations that shape the way
modalities co-occur over time. In this paper we study the dynamic nature of
multimedia and social-media information, where the temporal dimension emerges
as a strong source of evidence for learning the temporal correlations across
visual and textual modalities. So far, cross-media retrieval models, explored
the correlations between different modalities (e.g. text and image) to learn a
common subspace, in which semantically similar instances lie in the same
neighbourhood. Building on such knowledge, we propose a novel temporal
cross-media neural architecture, that departs from standard cross-media
methods, by explicitly accounting for the temporal dimension through temporal
subspace learning. The model is softly-constrained with temporal and
inter-modality constraints that guide the new subspace learning task by
favouring temporal correlations between semantically similar and temporally
close instances. Experiments on three distinct datasets show that accounting
for time turns out to be important for cross-media retrieval. Namely, the
proposed method outperforms a set of baselines on the task of temporal
cross-media retrieval, demonstrating its effectiveness for performing temporal
subspace learning.Comment: To appear in ACM MM 201
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
This paper provides a unified account of two schools of thinking in
information retrieval modelling: the generative retrieval focusing on
predicting relevant documents given a query, and the discriminative retrieval
focusing on predicting relevancy given a query-document pair. We propose a game
theoretical minimax game to iteratively optimise both models. On one hand, the
discriminative model, aiming to mine signals from labelled and unlabelled data,
provides guidance to train the generative model towards fitting the underlying
relevance distribution over documents given the query. On the other hand, the
generative model, acting as an attacker to the current discriminative model,
generates difficult examples for the discriminative model in an adversarial way
by minimising its discrimination objective. With the competition between these
two models, we show that the unified framework takes advantage of both schools
of thinking: (i) the generative model learns to fit the relevance distribution
over documents via the signals from the discriminative model, and (ii) the
discriminative model is able to exploit the unlabelled data selected by the
generative model to achieve a better estimation for document ranking. Our
experimental results have demonstrated significant performance gains as much as
23.96% on Precision@5 and 15.50% on MAP over strong baselines in a variety of
applications including web search, item recommendation, and question answering.Comment: 12 pages; appendix adde
Feature Fusion Vision Transformer for Fine-Grained Visual Categorization
The core for tackling the fine-grained visual categorization (FGVC) is to
learn subtle yet discriminative features. Most previous works achieve this by
explicitly selecting the discriminative parts or integrating the attention
mechanism via CNN-based approaches.However, these methods enhance the
computational complexity and make the modeldominated by the regions containing
the most of the objects. Recently, vision trans-former (ViT) has achieved SOTA
performance on general image recognition tasks. Theself-attention mechanism
aggregates and weights the information from all patches to the classification
token, making it perfectly suitable for FGVC. Nonetheless, the classifi-cation
token in the deep layer pays more attention to the global information, lacking
the local and low-level features that are essential for FGVC. In this work, we
proposea novel pure transformer-based framework Feature Fusion Vision
Transformer (FFVT)where we aggregate the important tokens from each transformer
layer to compensate thelocal, low-level and middle-level information. We design
a novel token selection mod-ule called mutual attention weight selection (MAWS)
to guide the network effectively and efficiently towards selecting
discriminative tokens without introducing extra param-eters. We verify the
effectiveness of FFVT on three benchmarks where FFVT achieves the
state-of-the-art performance.Comment: 9 pages, 2 figures, 3 table