Precise Event Spotting (PES) aims to identify events and their class from long, untrimmed videos, particularly in sports. The main objective of PES is to detect the event at the exact moment it occurs. Existing methods mainly rely on features from a large pre-trained network, which may not be ideal for the task. Furthermore, these methods overlook the issue of imbalanced event class distribution present in the data, negatively impacting performance in challenging scenarios. This paper demonstrates that an appropriately designed network, trained end-to-end, can outperform state-of-the-art (SOTA) methods. Particularly, we propose a network with a convolutional spatial-temporal feature extractor enhanced with our proposed Adaptive Spatio-Temporal Refinement Module (ASTRM) and a long-range temporal module. The ASTRM enhances the features with spatio-temporal information. Meanwhile, the long-range temporal module helps extract global context from the data by modeling long-range dependencies. To address the class imbalance issue, we introduce the Soft Instance Contrastive (Soft-Ic) loss that promotes feature compactness and class separation. Extensive experiments show that the proposed method is efficient and outperforms the SOTA methods, specifically in more challenging settings.

In bad weather conditions like fog and haze, the particles present in the atmosphere scatter incident light in different directions. As a result, image taken under these conditions suffers from reduced visibility, lack of contrast, as a result, it appears colorless. Image dehazing method tries to recover a haze-free portrayal of the given hazy image. In this paper we propose a method that dehazes a given image by comparing various output patches with the original hazy version and choosing the best one. The comparison is performed by our proposed dehazed patch quality comparator based on Convolutional Neural Network (CNN). To select the best dehazed patch we employ binary search. Quantitative and qualitative evaluations show that our method achieves good results in most of the cases, and are, on an average, comparable with state-of-the-art methods.

Images taken under fog or haze have their visibility reduced due to the existence of aerosols in the atmosphere. Image dehazing methods try to recover haze-free versions of these images by removing the effect of haze. Methods proposed till now are exclusively for daytime scene images or for night-time scene. The method we propose here can dehaze an image independent of whether it was captured during the day or night. To achieve this we have relaxed the image formation model to take into account spatially varying atmospheric light that may be present in night-time images. We estimate the contributing airlights and also the patches that are affected by it. We then remove the effect of airlight to obtain haze-free image. We demonstrate the results obtained by using our method on day and night-time images. We have compared out results with that of recently reported methods and the results show the effectiveness of our method.