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Zero shot learning in Image Classification refers to the setting where images from some novel classes are absent in the training data but other information such as natural language descriptions or attribute vectors of the classes are available. This setting is important in the real world since one may not be able to obtain images of all the possible classes at training. While previous approaches have tried to model the relationship between the class attribute space and the image space via some kind of a transfer function in order to model the image space correspondingly to an unseen class, we take a different approach and try to generate the samples from the given attributes, using a conditional variational autoencoder, and use the generated samples for classification of the unseen classes. By extensive testing on four benchmark datasets, we show that our model outperforms the state of the art, particularly in the more realistic generalized setting, where the training classes can also appear at the test time along with the novel classes.

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We present a generative framework for zero-shot action recognition where some of the possible action classes do not occur in the training data. Our approach is based on modeling each action class using a probability distribution whose parameters are functions of the attribute vector representing that action class. In particular, we assume that the distribution parameters for any action class in the visual space can be expressed as a linear combination of a set of basis vectors where the combination weights are given by the attributes of the action class. These basis vectors can be learned solely using labeled data from the known (i.e., previously seen) action classes, and can then be used to predict the parameters of the probability distributions of unseen action classes. We consider two settings: (1) Inductive setting, where we use only the labeled examples of the seen action classes to predict the unseen action class parameters; and (2) Transductive setting which further leverages unlabeled data from the unseen action classes. Our framework also naturally extends to few-shot action recognition where a few labelled examples from unseen classes are available. Our experiments on benchmark datasets (UCF101, HMDB51 and Olympic) show significant performance improvements as compared to various baselines, in both standard zero-shot (disjoint seen and unseen classes) and generalized zero-shot learning settings.

Video based computer vision tasks can benefit from estimation of the salient regions and interactions between those regions. Traditionally, this has been done by identifying the object regions in the images by utilizing pre-trained models to perform object detection, object segmentation and/or object pose estimation. Though using pre-trained models seems to be a viable approach, it is infeasible in practice due to the need for exhaustive annotation of object categories, domain gap between datasets and bias present in pre-trained models. To overcome these downsides, we propose to utilize the common rationale that a sequence of video frames capture a set of common objects and interactions between them, thus a notion of co-segmentation between the video frame features may equip the model with the ability to automatically focus on salient regions and improve underlying task's performance in an end-to-end manner. In this regard, we propose a generic module called "Co-Segmentation Activation Module" (COSAM) that can be plugged-in to any CNN to promote the notion of co-segmentation based attention among a sequence of video frame features. We show the application of COSAM in three video based tasks namely: 1) Video-based person re-ID, 2) Video captioning, & 3) Video action classification and demonstrate that COSAM is able to capture salient regions in the video frames, thus leading to notable performance improvements along with interpretable attention maps.

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We present an efficient multi stage approach to detection of deformable objects in real, cluttered images given a single or few hand drawn examples as models. The method handles deformations of the object by first breaking the given model into segments at high curvature points. We allow bending at these points as it has been studied that deformation typically happens at high curvature points. The broken segments are then scaled, rotated, deformed and searched independently in the gradient image. Point maps are generated for each segment that represent the locations of the matches for that segment. We then group k points from the point maps of k adjacent segments using a cost function that takes into account local scale variations as well as inter-segment orientations. These matched groups yield plausible locations for the objects. In the fine matching stage, the entire object contour in the localized regions is built from the k-segment groups and given a comprehensive score in a method that uses dynamic programming. An evaluation of our algorithm on a standard dataset yielded results that are better than published work on the same dataset. At the same time, we also evaluate our algorithm on additional images with considerable object deformations to verify the robustness of our method.

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