Are all the frames equally important?

In this work, we address the problem of measuring and predicting temporal video saliency - a metric which defines the importance of a video frame for human attention. Unlike the conventional spatial saliency which defines the location of the salient regions within a frame (as it is done for still images), temporal saliency considers importance of a frame as a whole and may not exist apart from context. The proposed interface is an interactive cursor-based algorithm for collecting experimental data about temporal saliency. We collect the first human responses and perform their analysis. As a result, we show that qualitatively, the produced scores have very explicit meaning of the semantic changes in a frame, while quantitatively being highly correlated between all the observers. Apart from that, we show that the proposed tool can simultaneously collect fixations similar to the ones produced by eye-tracker in a more affordable way. Further, this approach may be used for creation of first temporal saliency datasets which will allow training computational predictive algorithms. The proposed interface does not rely on any special equipment, which allows to run it remotely and cover a wide audience.Comment: CHI'20 Late Breaking Work

Rola innowacji w zrównoważonym systemie sanitarnym: studium przypadku Indii

Sanitation and water are one of those problems which have been given top priority in the sustainable agenda. However, scanty resources, geographical condition, natural environment, tradition, institutional and financial constraints lead to several challenges of feasibility, affordability, availability,and acceptability. This study reveals the inequality in the access to improved toilet facilities based on wealth index and locality of households using National Family Health Survey (NFHS) data. These problems can be addressed by applying different types of social innovations in which novelty in product and process can play a crucial role. This paper critically examines the role of innovation which can play in expanding transition to sustainable development in the sanitation sector which needs some financial, organizational, and institutional agreement. The progress in sanitation sector is dependent on the consumer behavior. However, it still lacks a variety of quality-price ranges and its utility as the basic needs of dignified life.Warunki sanitarne i woda to jedne z najważniejszych wyzwań w kontekście zrównoważonego rozwoju. Zarazem skąpe zasoby, warunki geograficzne, środowisko naturalne, tradycja, ograniczenia instytucjonalne i finansowe prowadzą do kilku wyzwań związanych z wykonalnością, przystępnością cenową, dostępnością i akceptowalnością. Badanie to ujawnia nierówności w dostępie do ulepszonych toalet w oparciu o indeks zamożności i lokalizację gospodarstw domowych na podstawie danych National Family Health Survey (NFHS). Problemy te można rozwiązać, stosując różne rodzaje innowacji społecznych, w których nowość w produkcie i procesie może odgrywać kluczową rolę. W artykule krytycznie przeanalizowano rolę innowacji, które mogą odegrać istotną rolę w przejściu do zrównoważonego rozwoju w sektorze sanitarnym, które wymaga finansowego, organizacyjnego i instytucjonalnego zabezpieczenia. Postęp w sektorze sanitarnym zależy też od zachowań konsumentów. Jednak nadal brakuje tu różnych przedziałów jakościowo-cenowych i  użyteczności zapewniających podstawowe potrzeby godnego życia

Sparse Methods for Robust and Efficient Visual Recognition

Visual recognition has been a subject of extensive research in computer vision. A vast literature exists on feature extraction and learning methods for recognition. However, due to large variations in visual data, robust visual recognition is still an open problem. In recent years, sparse representation-based methods have become popular for visual recognition. By learning a compact dictionary of data and exploiting the notion of sparsity, start-of-the-art results have been obtained on many recognition tasks. However, existing data-driven sparse model techniques may not be optimal for some challenging recognition problems. In this dissertation, we consider some of these recognition tasks and present approaches based on sparse coding for robust and efficient recognition in such cases. First we study the problem of low-resolution face recognition. This is a challenging problem, and methods have been proposed using super-resolution and machine learning based techniques. However, these methods cannot handle variations like illumination changes which can happen at low resolutions, and degrade the performance. We propose a generative approach for classifying low resolution faces, by exploiting 3D face models. Further, we propose a joint sparse coding framework for robust classification at low resolutions. The effectiveness of the method is demonstrated on different face datasets. In the second part, we study a robust feature-level fusion method for multimodal biometric recognition. Although score-level and decision-level fusion methods exist in biometric literature, feature-level fusion is challenging due to different output formats of biometric modalities. In this work, we propose a novel sparse representation-based method for multimodal fusion, and present experimental results for a large multimodal dataset. Robustness to noise and occlusion are demonstrated. In the third part, we consider the problem of domain adaptation, where we want to learn effective classifiers for cases where the test images come from a different distribution than the training data. Typically, due to high cost of human annotation, very few labeled samples are available for images in the test domain. Specifically, we study the problem of adapting sparse dictionary-based classification methods for such cases. We describe a technique which jointly learns projections of data in the two domains, and a latent dictionary which can succinctly represent both domains in the projected low dimensional space. The proposed method is efficient and performs on par or better than many competing state-of-the-art methods. Lastly, we study an emerging analysis framework of sparse coding for image classification. We show that the analysis sparse coding can give similar performance as the typical synthesis sparse coding methods, while being much faster at sparse encoding. In the end, we conclude the dissertation with discussions and possible future directions

SALAD: Source-free Active Label-Agnostic Domain Adaptation for Classification, Segmentation and Detection

We present a novel method, SALAD, for the challenging vision task of adapting a pre-trained "source" domain network to a "target" domain, with a small budget for annotation in the "target" domain and a shift in the label space. Further, the task assumes that the source data is not available for adaptation, due to privacy concerns or otherwise. We postulate that such systems need to jointly optimize the dual task of (i) selecting fixed number of samples from the target domain for annotation and (ii) transfer of knowledge from the pre-trained network to the target domain. To do this, SALAD consists of a novel Guided Attention Transfer Network (GATN) and an active learning function, HAL. The GATN enables feature distillation from pre-trained network to the target network, complemented with the target samples mined by HAL using transfer-ability and uncertainty criteria. SALAD has three key benefits: (i) it is task-agnostic, and can be applied across various visual tasks such as classification, segmentation and detection; (ii) it can handle shifts in output label space from the pre-trained source network to the target domain; (iii) it does not require access to source data for adaptation. We conduct extensive experiments across 3 visual tasks, viz. digits classification (MNIST, SVHN, VISDA), synthetic (GTA5) to real (CityScapes) image segmentation, and document layout detection (PubLayNet to DSSE). We show that our source-free approach, SALAD, results in an improvement of 0.5%-31.3%(across datasets and tasks) over prior adaptation methods that assume access to large amounts of annotated source data for adaptation

Molecular association of glucose-6- phosphate isomerase and pyruvate kinase M2 with glyceraldehyde-3-phosphate dehydrogenase in cancer cells

Background: For a long time cancer cells are known for increased uptake of glucose and its metabolization through glycolysis. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) is a key regulatory enzyme of this pathway and can produce ATP through oxidative level of phosphorylation. Previously, we reported that GAPDH purified from a variety of malignant tissues, but not from normal tissues, was strongly inactivated by a normal metabolite, methylglyoxal (MG).Molecular mechanism behind MG mediated GAPDH inhibition in cancer cells is not well understood. Methods: GAPDH was purified from Ehrlich ascites carcinoma (EAC) cells based on its enzymatic activity. GAPDH associated proteins in EAC cells and 3-methylcholanthrene (3MC) induced mouse tumor tissue were detected by mass spectrometry analysis and immunoprecipitation (IP) experiment, respectively. Interacting domains of GAPDH and its associated proteins were assessed by in silico molecular docking analysis. Mechanism of MG mediated GAPDH inactivation in cancer cells was evaluated by measuring enzyme activity, Circular dichroism (CD) spectroscopy, IP and mass spectrometry analyses. Result: Here, we report that GAPDH is associated with glucose-6-phosphate isomerase (GPI) and pyruvate kinase M2 (PKM2) in Ehrlich ascites carcinoma (EAC) cells and also in 3-methylcholanthrene (3MC) induced mouse tumor tissue. Molecular docking analyses suggest C-terminal domain preference for the interaction between GAPDH and GPI. However, both C and N termini of PKM2 might be interacting with the C terminal domain of GAPDH. Expression of both PKM2 and GPI is increased in 3MC induced tumor compared with the normal tissue. In presence of 1 mM MG,association of GAPDH with PKM2 or GPI is not perturbed, but the enzymatic activity of GAPDH is reduced to 26.8 ± 5 % in 3MC induced tumor and 57.8 ± 2.3 % in EAC cells. Treatment of MG to purified GAPDH complex leads to glycation at R399 residue of PKM2 only, and changes the secondary structure of the protein complex. Conclusion: PKM2 may regulate the enzymatic activity of GAPDH. Increased enzymatic activity of GAPDH in tumor cells may be attributed to its association with PKM2 and GPI. Association of GAPDH with PKM2 and GPI could be a signature for cancer cells. Glycation at R399 of PKM2 and changes in the secondary structure of GAPDH complex could be one of the mechanisms by which GAPDH activity is inhibited in tumor cells by MG