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Furthermore, many of the aforementioned approaches rely on the split-and-aggregate approach when processing long videos, where inputs are divided into sections which are processed separately by the model. Though many of the downstream tasks considered by these models do not specifically require temporal reasoning over the input videos, their applicability to tasks requiring temporal reasoning is limited, as it keeps models from capturing interactions across segments. In light of this, we present Locformer, a Transformer-based model for the task of temporal moment localization which operates at a constant memory footprint regardless of the input length, as shown in Figure 1. The success of the Locformer relies on two key ideas. Firstly, Locformer incorporates Stochastic Bucket-wise Feature Sampling (SBFS), which splits the sequence of input video feature into a fixed number of buckets and selects a single feature per bucket per iteration using a stochastic approach during training. While bucketing enables us to keep the memory budget limited by effectively shortening the input sequence length to the number of buckets, the stochastic nature of our approach allows us to obtain a better coverage of the video with sufficient stochasticity, obtaining a feature sample-set that is representative of the video content for the task at hand. You can watch the following animation to learn more about the basics of sound. Click the arrow to move on to the next slide. Acoustic levitation uses sound traveling through a fluid -- usually a gas -- to balance the force of gravity. On Earth, this can cause objects and materials to hover unsupported in the air. In space, it can hold objects steady so they don't move or drift. The process relies on of the properties of sound waves, especially intense sound waves. We'll look at how sound waves become capable of lifting objects in the next section. A basic acoustic levitator has two main parts -- a transducer, which is a vibrating surface that makes sound, and a reflector. Often, the transducer and reflector have concave surfaces to help focus the sound. A sound wave travels away from the transducer. Bounces off the reflector. Three basic properties of this traveling, reflecting wave help it to suspend objects in midair.|The gap in representations between image and video makes Image-to-Video Re-identi?cation (I2V Re-ID) challenging, and recent works formulate this problem as a knowledge distillation (KD) process. In this paper, we propose a mutual discriminative knowledge distillation framework to transfer a video-based richer representation to an image based representation more effectively. Specifically, we propose the triplet contrast loss (TCL), a novel loss designed for KD. During the KD process, the TCL loss transfers the local structure, exploits the higher order information, and mitigates the misalignment of the heterogeneous output of teacher and student networks. Compared with other losses for KD, the proposed TCL loss selectively transfers the local discriminative features from teacher to student, making it effective in the ReID. Besides the TCL loss, we adopt mutual learning to regularize both the teacher and student networks training. Extensive experiments demonstrate the effectiveness of our method on the MARS, DukeMTMC-VideoReID and VeRi-776 benchmarks. la liga table

















































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