Versus Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Say More Creator Hub Gift Ideas For Everyone On Your List Mashable Selects Switch Off Trending Now Safety Net In My Bag VidCon with Mashable All Series Google Pixel 11 Pro's HiLight feature is a good start, but it shouldn't be Pro-exclusive Alex Perry is a tech reporter at Mashable who primarily covers video games and consumer tech. Alex has spent most of the last decade reviewing games, smartphones, headphones, and laptops, and he doesn't plan on stopping anytime soon. He is also a Pisces, a cat lover, and a Kansas City sports fan. In an era of software innovations, Google has one neat hardware trick in the Pixel 11 lineup. It's a shame it's not part of the whole lineup, though.

Versus Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Say More Creator Hub Gift Ideas For Everyone On Your List Mashable Selects Switch Off Trending Now Safety Net In My Bag VidCon with Mashable All Series Pixel 11 Pro has one feature that (most) of the competition can't imitate. Alex Perry is a tech reporter at Mashable who primarily covers video games and consumer tech. Alex has spent most of the last decade reviewing games, smartphones, headphones, and laptops, and he doesn't plan on stopping anytime soon. He is also a Pisces, a cat lover, and a Kansas City sports fan. In an age when annual phone upgrades seem increasingly iterative, Google has brought at least one interesting new hardware feature to the table with the Pixel 11 Pro.

I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen The new Google Pixel 11 Pro models feature a useful HiLight indicator that pays homage to classic notification LEDs. Pixel 11 Pro models have a pulsing light indicator on the camera bump. HiLight pulses in response to incoming calls and Gemini interactions. Expanded functionality may be coming, but don't expect it at launch. I remember when life was much simpler, when I could just plop my phone down and know exactly when and how to respond to it depending on the color of its teensy-weensy LED indicator.

Look Up Say More Versus Creator Hub Switch Off Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Trending Now Safety Net In My Bag VidCon with Mashable Back to School Furtastic All Series Alex Perry is a tech reporter at Mashable who primarily covers video games and consumer tech. Alex has spent most of the last decade reviewing games, smartphones, headphones, and laptops, and he doesn't plan on stopping anytime soon. He is also a Pisces, a cat lover, and a Kansas City sports fan. How much different will it be from the Pixel 10 pictured here? Samsung announced a bunch of fancy new phones, but don't relax just yet.

Hierarchical text classification (HTC) is a special sub-task of multi-label classification (MLC) whose taxonomy is constructed as a tree and each sample is assigned with at least one path in the tree. Latest HTC models contain three modules: a text encoder, a structure encoder and a multi-label classification head. Specially, the structure encoder is designed to encode the hierarchy of taxonomy. However, the structure encoder has scale problem. As the taxonomy size increases, the learnable parameters of recent HTC works grow rapidly. Recursive regularization is another widely-used method to introduce hierarchical information but it has collapse problem and generally relaxed by assigning with a small weight (ie. 1e-6). In this paper, we propose a Hierarchy-aware Light Global model with Hierarchical local conTrastive learning (HiLight), a lightweight and efficient global model only consisting of a text encoder and a multi-label classification head. We propose a new learning task to introduce the hierarchical information, called Hierarchical Local Contrastive Learning (HiLCL). Extensive experiments are conducted on two benchmark datasets to demonstrate the effectiveness of our model.

A recent line of MLLMs work[1, 2] has started to explore introducing video modality. On the one hand, video contains more information than images because of the time-dimensional modelling capability, and video is much better in capturing events. On the other hand video cameras are everywhere around us as a common and low-cost electronic surveillance tool. However, how to transform advanced academic results or models into beneficial and practical products is a subject of much greater concern to industry. According to business requirement of the company, our team will focus on a challenging task, video understanding of billiards indoor scenes. We propose HiLight, a video chat model. HiLight is constructed by the two stages, each of which is required to accomplish respective target performance: 1. Video and Text Modality Alignment Stage: This stage requires aligning the video modality with the text modality as well as a strong ability to detecting small objects.