Video-text Large Language Models (video-text LLMs) have shown remarkable performance in answering questions and holding conversations on simple videos. However, they perform almost the same as random on grounding text queries in long and complicated videos, having little ability to understand and reason about temporal information, which is the most fundamental difference between videos and images. In this paper, we propose HawkEye, one of the first video-text LLMs that can perform temporal video grounding in a fully text-to-text manner. To collect training data that is applicable for temporal video grounding, we construct InternVid-G, a large-scale video-text corpus with segment-level captions and negative spans, with which we introduce two new time-aware training objectives to video-text LLMs. We also propose a coarse-grained method of representing segments in videos, which is more robust and easier for LLMs to learn and follow than other alternatives. Extensive experiments show that HawkEye is better at temporal video grounding and comparable on other video-text tasks with existing video-text LLMs, which verifies its superior video-text multi-modal understanding abilities.

While "M*A*S*H" was known for its snappy humor and lively dialogue, ChatGPT's effort was hollow and its jokes leaden at best. But it was the first time the two characters interacted since the 1983 series finale, which aired almost exactly 40 years ago and remains the most watched non-Super Bowl program ever broadcast on American TV. Hawkeye: My shorts -- the ones I wear every time I have important surgery. I know you took them. B.J.: I wouldn't be caught dead in your underwear.

The 911 caller reported seeing a man wrestle a woman to the ground in an attempt to rob her before taking off on a bicycle. Beverly Hills police responded, but before a patrol car could get to the scene, officers already had their eyes on the suspect. The police recently added a new drone known as "Hawkeye" to its drone patrol that will give officers a view of crime scenes before they arrive, locate suspects before they're lost, and help patrol the streets of the upscale community. The drone's high-resolution camera is capable of reading a license plate a half-mile away. The city's police department introduced Hawkeye to the city on Tuesday in a set of social media posts that included video of the drone in action.

mmWave radar has been shown as an effective sensing technique in low visibility, smoke, dusty, and dense fog environment. However tapping the potential of radar sensing to reconstruct 3D object shapes remains a great challenge, due to the characteristics of radar data such as sparsity, low resolution, specularity, high noise, and multi-path induced shadow reflections and artifacts. In this paper we propose 3D Reconstruction and Imaging via mmWave Radar (3DRIMR), a deep learning based architecture that reconstructs 3D shape of an object in dense detailed point cloud format, based on sparse raw mmWave radar intensity data. The architecture consists of two back-to-back conditional GAN deep neural networks: the first generator network generates 2D depth images based on raw radar intensity data, and the second generator network outputs 3D point clouds based on the results of the first generator. The architecture exploits both convolutional neural network's convolutional operation (that extracts local structure neighborhood information) and the efficiency and detailed geometry capture capability of point clouds (other than costly voxelization of 3D space or distance fields). Our experiments have demonstrated 3DRIMR's effectiveness in reconstructing 3D objects, and its performance improvement over standard techniques.

This problem is highlighted and exacerbated by the episodic nature of the Hawkeye and Kate Bishop story line, and how disjointed it feels from the rest of the Avengers. Kate Bishop and Hawkeye barely talk to any of the other Avengers, leaving me to assume that Crystal Dynamics couldn't get additional lines from the original voice talent. The final enemy of Hawkeye's story is Maestro Hulk, a demented future version of Bruce Banner. But when I bring my own Hulk to the fight, nobody has anything to say about having two Hulks in the room, not even either of the Hulks. Much of the story is about Clint Barton (a.k.a.

Keysight Technologies' business Ixia has incorporated machine learning technologies into its network monitoring platform. The new technologies will help the platform, called Hawkeye, to help enterprises reduce network outage times and improve network uptime by detecting, identifying, and resolving network anomalies. The company cites statistics from Gartner, which predicts that more than 50% of new enterprise applications will incorporate machine learning or other intelligence models. Ixia also notes that as the volume and velocity of raw network and application data continues to increase, network operations teams are faced with a flood of alerts. These teams need to reduce alert fatigue and increase their ability to troubleshoot network and application issues.

The Iowa Hawkeyes are on the forefront of technological advances when it comes to their practice facilities, and this weekend, they're showing off their newest toy. The Hawkeyes are using a technology called "Seeker," which is the first robotic quarterback, and it can also handle punting and kicking drills, too. Not only is the Seeker the world's first robotic quarterback, it's also the world's first robotic punter and kicker. Tired of wasted reps during Special Teams periods? Take the guesswork out of the equation and make each rep count.