  Video retrieval using natural language queries requires learning semantically
meaningful joint embeddings between the text and the audio-visual input. Often,
such joint embeddings are learnt using pairwise (or triplet) contrastive loss
objectives which cannot give enough attention to 'difficult-to-retrieve'
samples during training. This problem is especially pronounced in data-scarce
settings where the data is relatively small (10% of the large scale MSR-VTT) to
cover the rather complex audio-visual embedding space. In this context, we
introduce Rudder - a multilingual video-text retrieval dataset that includes
audio and textual captions in Marathi, Hindi, Tamil, Kannada, Malayalam and
Telugu. Furthermore, we propose to compensate for data scarcity by using domain
knowledge to augment supervision. To this end, in addition to the conventional
three samples of a triplet (anchor, positive, and negative), we introduce a
fourth term - a partial - to define a differential margin based partialorder
loss. The partials are heuristically sampled such that they semantically lie in
the overlap zone between the positives and the negatives, thereby resulting in
broader embedding coverage. Our proposals consistently outperform the
conventional max-margin and triplet losses and improve the state-of-the-art on
MSR-VTT and DiDeMO datasets. We report benchmark results on Rudder while also
observing significant gains using the proposed partial order loss, especially
when the language specific retrieval models are jointly trained by availing the
cross-lingual alignment across the language-specific datasets.
