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Abdel-Karim Al-Tamimi

Publications and source records attributed to Abdel-Karim Al-Tamimi.

4 recordsLinked to original sources
arXiv2026

TellTale: Blending Multi-Instance LoRA Text Encoders and a Zero-Shot LLM Judge for Ambivalence/Hesitancy Recognition in Videos

We present TellTale, a text-only approach to ambivalence/hesitancy (A/H) recognition in interview videos, evaluated on the BAH dataset as part of the 3rd A/H Video Recognition Challenge (11th ABAW Workshop, ECCV 2026). Although the dataset provides video, audio, facial crops, and transcripts, TellTale relies on the transcript alone and combines three probability streams. Two text encoders, multilingual-e5-large and mDeBERTa-v3-base, are fine-tuned with parameter-efficient LoRA adapters under a multiple-instance learning (MIL) objective, in which transcript chunks are scored individually and pooled with a smooth maximum so that only the video-level label is needed for supervision. The third stream requires no training: a quantized 14B instruction LLM is prompted, zero-shot, to rate each transcript for A/H. The three probabilities are combined by a weighted average and a single decision threshold, both selected on participant-grouped cross-validated predictions. On the organizer-scored private test set of 152 videos from unseen participants, TellTale achieves a Macro-F1 of 0.7364 and an average precision of 0.7940, compared with 0.2827 Macro-F1 for the official vision-based baseline.

cs.CV↗
arXiv2016

QoE-Based Low-Delay Live Streaming Using Throughput Predictions

Recently, HTTP-based adaptive streaming has become the de facto standard for video streaming over the Internet. It allows clients to dynamically adapt media characteristics to network conditions in order to ensure a high quality of experience, that is, minimize playback interruptions, while maximizing video quality at a reasonable level of quality changes. In the case of live streaming, this task becomes particularly challenging due to the latency constraints. The challenge further increases if a client uses a wireless network, where the throughput is subject to considerable fluctuations. Consequently, live streams often exhibit latencies of up to 30 seconds. In the present work, we introduce an adaptation algorithm for HTTP-based live streaming called LOLYPOP (Low-Latency Prediction-Based Adaptation) that is designed to operate with a transport latency of few seconds. To reach this goal, LOLYPOP leverages TCP throughput predictions on multiple time scales, from 1 to 10 seconds, along with an estimate of the prediction error distribution. In addition to satisfying the latency constraint, the algorithm heuristically maximizes the quality of experience by maximizing the average video quality as a function of the number of skipped segments and quality transitions. In order to select an efficient prediction method, we studied the performance of several time series prediction methods in IEEE 802.11 wireless access networks. We evaluated LOLYPOP under a large set of experimental conditions limiting the transport latency to 3 seconds, against a state-of-the-art adaptation algorithm from the literature, called FESTIVE. We observed that the average video quality is by up to a factor of 3 higher than with FESTIVE. We also observed that LOLYPOP is able to reach a broader region in the quality of experience space, and thus it is better adjustable to the user profile or service provider requirements.

cs.NI↗
arXiv2016

Low-Delay Adaptive Video Streaming Based on Short-Term TCP Throughput Prediction

Recently, HTTP-Based Adaptive Streaming has become the de facto standard for video streaming over the Internet. It allows the client to adapt media characteristics to varying network conditions in order to maximize Quality of Experience (QoE). In the case of live streaming this task becomes particularly challenging. An important factor than might help improving performance is the capability to correctly predict network throughput dynamics on short to medium timescales. It becomes notably difficult in wireless networks that are often subject to continuous throughput fluctuations. In the present work, we develop an adaptation algorithm for HTTP-Based Adaptive Live Streaming that, for each adaptation decision, maximizes a QoE-based utility function depending on the probability of playback interruptions, average video quality, and the amount of video quality fluctuations. To compute the utility function the algorithm leverages throughput predictions, and dynamically estimated prediction accuracy. We are trying to close the gap created by the lack of studies analyzing TCP throughput on short to medium timescales. We study several time series prediction methods and their error distributions. We observe that Simple Moving Average performs best in most cases. We also observe that the relative underestimation error is best represented by a truncated normal distribution, while the relative overestimation error is best represented by a Lomax distribution. Moreover, underestimations and overestimations exhibit a temporal correlation that we use to further improve prediction accuracy. We compare the proposed algorithm with a baseline approach that uses a fixed margin between past throughput and selected media bit rate, and an oracle-based approach that has perfect knowledge over future throughput for a certain time horizon.

cs.NI↗